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2014 4 Naveen Kumar Sidda Towards Geo Decision Support Systems for Renewable Energy Outreach Departamento Director/es Informática e Ingeniería de Sistemas López Pellicer, Francisco Javier Zarazaga Soria, Francisco Javier Director/es Tesis Doctoral Autor Repositorio de la Universidad de Zaragoza – Zaguan http://zaguan.unizar.es UNIVERSIDAD DE ZARAGOZA Departamento Director/es Naveen Kumar Sidda TOWARDS GEO DECISION SUPPORT SYSTEMS FOR RENEWABLE ENERGY OUTREACH Director/es Informática e Ingeniería de Sistemas López Pellicer, Francisco Javier Zarazaga Soria, Francisco Javier Tesis Doctoral Autor 2013 Repositorio de la Universidad de Zaragoza – Zaguan http://zaguan.unizar.es UNIVERSIDAD DE ZARAGOZA Departamento Director/es Director/es Tesis Doctoral Autor Repositorio de la Universidad de Zaragoza – Zaguan http://zaguan.unizar.es UNIVERSIDAD DE ZARAGOZA TOWARDS GEO DECISION SUPPORT SYSTEMS FOR RENEWABLE ENERGY OUTREACH Naveen Kumar Sidda PhD DISSERTATION RESEARCH ADVISIORS Dr. Francisco Javier Lopez-Pellicer Dr. Francisco Javier Zarazaga-Soria December 2013 Computer Science and Systems Engineering Department Universidad de Zaragoza, Spain ii iii "A visionary mind is like a ride in a forest, it has to be sharp to explore best path to reach the destination whereas a conventional mind is like a ride on a highway, programmed to a set of speed limits and diversion boards. I thrive to be a visionary." -Naveen Sidda iv v Acknowledgements This thesis owes its existence to the help, support and inspiration from many people. In first place, I would like to express heartfelt appreciation and gratitude to my supervisor Dr. F. Javier Zarazaga-Soria for his wisdom and expert guidance from the very first day of the meeting till the last day of the final defence and Dr. Francisco J. Lopez-Pellicer for his sharp and constructive criticisms, keeping me sane amidst the stress-infested thesis days and for keeping my mind at peace. I am profoundly grateful to Mr. Borja A. Espejo García computer science expert for his expertise, time and cooperation that contributed substantial in developing the tool and also for his efforts in documentation the tool technical design. I would like to thank to all the members of Advanced Information Systems Laboratory (IAAA) of the Computer science and System Engineering Department of the University of Zaragoza, and its spin-off GeoSpatiumLab for their support and assistance which brought the whole project together. Very special thanks to Covadonga, Walter and Aneta for helping me with the bureaucratic procedures and making my stay pleasant and happy. I would like to extend my thanks to Dr. Pedro R. Muro-Medrano for giving me permissions to go ahead with the work and the thesis. Finally, I would like to thank all the thesis evaluation experts and people who reviewed my thesis. I also take the opportunity to owe sincere gratitude to my family for continuous and unconditional support of all my undertakings and scholastic. vi xiii Bibliography .........................................................................................................................................95 xiv List of Tables Table 1: Different sources of energy and their CO2 emissions (Maaßen et.al 2011) .................... 2 Table 2: Solar radiation sources (Upington Solar Park 2011). ........................................................... 24 Table 3: Suggested technology for 3D data based on the application requirements (Zhenhua and Ioannis 2009) ...................................................................................................................................... 30 Table 4: Data sources and various application areas (Omar and Ayhan 2009) ........................... 31 xv List of Figures Figure 1: Profile analytics for decision makers .......................................................................................... 3 Figure 2: Decision-making architecture (Passionned Group 2013) ................................................... 5 Figure 3: Applying technology to scientific data ........................................................................................ 6 Figure 4: Research methodology ................................................................................................................... 11 Figure 5: Overall basic architecture of the REDSS ................................................................................... 13 Figure 6 : Abstract sequential view of the chapters. ............................................................................... 14 Figure 7: Context maturity verses Model maturity ................................................................................. 18 Figure 8: DSS as a solution for model and context maturity ................................................................ 19 Figure 9: Importance of DSS for various sun-shadow use cases (Inspired from GoGeomatics 2013). ............................................................................................................................................................. 20 Figure 10: General scheme for expeditious management plan .......................................................... 28 Figure 11: Illustrates the stages involved in making the digital earth model .............................. 34 Figure 12: 3D model of the Zaragoza city ................................................................................................... 35 Figure 13: BI modules for REDSS (Olszak and Ziemba 2007). ............................................................ 39 Figure 14: Conceptual Architecture of REDSS ecosystem ..................................................................... 41 Figure 15: Illustrates decomposing architectures into components .............................................. 42 Figure 16: Star schema for Shadow cube .................................................................................................... 44 Figure 17: User maturity verses Data maturity ........................................................................................ 45 Figure 18: Star schema for Wind atlas ......................................................................................................... 46 Figure 19: Venn diagram for Wind and Solar energy location............................................................ 47 Figure 20: Interface of Energy2people application. ............................................................................... 54 Figure 21: Multiple options to analyse the solar radiation data ........................................................ 55 Figure 22: Data flow diagram. ......................................................................................................................... 56 Figure 23: Load process in detail. .................................................................................................................. 57 Figure 24: Scraper class diagram.................................................................................................................. 58 Figure 25: Interpolation process class diagram. ...................................................................................... 59 Figure 26: ETL using ESRI-Shapefiles. .......................................................................................................... 60 Figure 27: Application example: Zaragoza. ................................................................................................ 60 Figure 28: SDW conceptual model................................................................................................................ 61 Figure 29: SDW logic model ............................................................................................................................ 62 Figure 30: Abstraction of the future goal. ................................................................................................... 63 Figure 31: Future SDW logic model. ............................................................................................................. 63 Figure 32: Future SDW logic model .............................................................................................................. 64 Figure 33: Aggregate tables design ............................................................................................................... 65 Figure 34: Business logic class diagram. ..................................................................................................... 66 Figure 35: Web Scraper configuration file. ................................................................................................ 67 Figure 36: How Scraper interacts with the NASA website. .................................................................. 68 Figure 37: First NASA website page. ............................................................................................................. 69 Figure 38: Second NASA website page. ........................................................................................................ 70 Figure 39: The text file with the renewable energy data. ..................................................................... 70 Figure 40: Interpolation configuration file. ............................................................................................... 72 Figure 41: Example of interpolations levels. ............................................................................................. 72 Figure 42: The summary of the Interpolation process. ......................................................................... 73 Figure 43: Graphic summarizing Load process. ....................................................................................... 74 Figure 44: ETL flow implementation in Geokettle .................................................................................. 75 Figure 45: A graphic view of the implemented cube. ............................................................................. 77 Figure 46: XML file of the implemented cube............................................................................................ 78 Figure 47: XML file with the future spatial dimension. ......................................................................... 79 xvi Figure 48: NREL single zoom visualization (top) Energy2people multi zoom visualization (bottom). ....................................................................................................................................................... 85 Figure 49: Energy2people multiscale geographical representation of data ................................. 86 Figure 50: Energy2People visualization formats .................................................................................... 87 Figure 51: Graphical relationship of thesis chapters, questions (Q) & contributions (C) ........ 89 Figure 52: Checkerboard graphs for Global Horizontal Irradiance (Kaku and Potter 2009). 93 xvii Nomenclature BI Business Intelligence BPM Business Process Management CSS Cascading Style Sheets DAO Data Access Object DSS Decision Support System DEM Digital Elevation Models DTM Digital Terrain Models DNI Direct Normal Irradiance DOM Document Object Model ER Entity Relationship ETL Extract Transform and Load GRASS Geographic Resources Analysis Support System GHI Global Horizontal Irradiance HOLAP Hybrid Online Analytical Processing HTTP Hypertext Transfer Protocol IEA International Energy Agency IRENA International Renewable Energy Agency JAI Java Advanced Imaging JAK Java API for KML LIDAR Light Detection and Ranging MDX Multidimensional Expressions MOLAP Multidimensional Online Analytical Processing MCDA Multi Criteria Decision Analysis NASA National Aeronautics and Space Administration NREL National Renewable Energy Laboratory OLAP Online Analytical Processing xviii OGC Open Geospatial Consortium PV Photovoltaic QA Quality Assurance QC Quality Check ROLAP Relational Online Analytical Processing REDSS Renewable Energy Decision Support System SDI Spatial Data Infrastructures SDW Spatial Data Warehouse SOLAP Spatial Online Analytical Processing SRID Spatial Reference System Identifier SPA Sun position Algorithm VGI Volunteered Geographic Information Chapter 1 INTRODUCTION Earth is affected by various natural phenomena such as natural calamities, disasters and by human actions like urbanization, pollution, etc. All these activities tremendously affect earth’s natural resources leading to shortage of resources. One such major issue is exhaustive usage of fossil energy and its adverse impact on our environment (Jinux and Somani 2010). Therefore, it is imperative to seek for secondary clean energy resources to meet our needs and reduce the dependence on fossil energy resources. As per the Table 1, transforming from fossil energy infrastructure to clean renewable energy resources such as wind, solar and hydro will be environment friendly as there is little global warming emissions and reduces dependence on fossil energy sources. Renewable energy is a natural source of energy that has substantial benefits as it provides reliable energy system, stable energy prices, jobs, economic benefits and is environmentally friendly (Ren and Xue 2010). Essentially, there are two kinds of renewable energy sources: continuous (biomass, geothermal, and hydro) and intermittent (solar and wind) (C2es 2012). The study on energy resources and their environmental impact is very essential for the development of a sustainable society (Jordanger et al. 2005). Current energy production is mainly depended on fossil energy, which is the main source for emission of green house gases such as CO2 (Maaßen et.al 2011). This is evident from Table 1; it also shows different sources of energy and their estimated CO2 emissions. The use of renewable energy spares conventional energy resources such as oil, coal, and natural gas from over exhaustion and reduces environmental pollution (Zhao and Ma 2011). Just like any other sources, usage of renewable energies also has its own benefits and limitations. Renewable energy sources are clean and environmental friendly, freely available abundant regenerative resources (Goel et al. 2010). These resources can even generate power in remote areas with zero or low negative impact on the environment and has substantial benefits on health, climate and economy (Mohideen 2012). On other hand, it is not economical to transport renewable energy to longer distance; it is not a stable supply source, which results in demand and supply imbalance (Jinxu and Somani 2010, Nagaraj 2012). In addition, frequent changes in renewable energy policies and natural calamities affects on renewable energy farms and eventually on the commercial aspects of the industry (Xia and Xia 2010). However, as per the Key World Energy Statistics from the International Energy Agency (IEA), there is steady increase in usage of renewable energy, which shows as a positive sign for development in this sector (International Energy Agency 2011). INTRODUCTION 2 Technology Capacity/configuration/fuel Estimate (gCO2/kW h) Wind 2.5MW, offshore 9 Hydroelectric 3.1 MW. reservoir 10 Wind 1.5 MW, onshore 10 Biogas Anaerobic digestion 11 Hydroelectric 300 kW, run-of-river 13 Solar thermal 80 MW, parabolic trough 13 Biomass Forest wood Co-combustion with hard coal 14 Biomass Forest wood steam turbine 22 Biomass Short rotation forestry Co-combustion with hard coal 23 Biomass Forest wood reciprocating engine 27 Biomass Waste wood steam turbine 31 Solar PV Polycrystalline silicone 32 Biomass Short rotation forestry steam turbine 35 Geothermal 80 MW, hot dry rock 38 Biomass Short rotation forestry reciprocating engine 41 Nuclear Various reactor types 66 Natural gas Various combined cycle turbines 443 Fuel cell Hydrogen from gas reforming 664 Diesel Various generator and turbine types 778 Heavy oil Various generator and turbine types 778 Coal Various generator types with scrubbing 960 Coal Various generator types without scrubbing 1050 Table 1: Different sources of energy and their CO2 emissions (Maaßen et.al 2011) As per the Table 1, solar energy is one of the best forms of renewable energy. Sun is the natural and vital source of human existence on the earth and it impacts all chemical, physical and biological processes. One hour of the sun’s energy on the earth is enough to power the entire planet for a year (Sharpe 2003). Sun’s energy or solar radiation and its geographical presence are measure to determine potential solar energy investments and development strategies (Shunbao et.al 2012). Solar energy is one of the eco-friendly energy production technologies, which reduces greenhouse gas emissions and improves environmental conditions of the society (Hohmeyer 1994, Al Otaibi and Al Jandal 2011). Besides, sun being an energy source, it also has very interesting relation with earth and objects on the surface of the earth. The idea of connecting and mapping this interesting relationship led to formulate the central research theme of this thesis. The sun’s position, it’s casting shadows, its topological relation; mapping this data to the topographic data is vital for various existing uses like photovoltaic cells and also for various other application scenarios presented in this thesis. In general, decision-making is process of selecting the best viable option from a set of different possible ways of doing things. Profile analytics is a way to analyse, assess a dataset, a person or a product based on the profile historic for decision-making (Schiaffino and Amandi 2009). Profile analytics can be defined as analysing historical records to identify data patterns and behaviours over a period of time and location for decision-making process. For example, to hire a professional for a job, usually requirements will be number of years of experience, past records, kinds of projects, valuable contributions made in the projects, location and 3 INTRODUCTION qualifications. Location is also a very important to hasten the progress of decision-making. For some key positions in a project, international experience is must as it adds value to the business. Whereas, for roles like field engineers, it is important to hire local people for various reasons like local language proficiency, better ground truth expertise etc. To screen for best candidate, Human Resources team needs all historical records of the candidature to analyse the performance, eligibility criteria and values. In addition, a historical trend of a person is also necessary for internal programs like Performance Improvement Plan to monitor and improve the performance of an employee (University of Texas, 2013). Similar mechanism applies to datasets as well for getting expected results and budget estimation. Likewise, study on spatiotemporal characteristics of the data should aid in having different views on data for agile decision-making. The Figure 1 below shows entirely two different concepts (human resources and data resources) but employing same historical analytics to seek the best result. Profile analytical mechanism (Business view and Scientific view) helps to answer “which, when & where” scenarios to managers and “why” to scientists. For example, among all, which is the best profile suitable for renewable energy manager position? Where is the best place to invest in both solar and wind farms? When was the last time highest productivity recorded? Why is this location not suitable for hydro projects? Why is there a dip in solar radiation in year 2000 versus 2012; January versus August? Figure 1: Profile analytics for decision makers From a business investment proposal to complex scientific analysis decision-making ecosystem is very important in projects. Decision Support System (DSS) is a cognitive ecosystem that facilitates deep, meaningful and useful interaction between humans and data for cost and time effective solutions (Vicki 2011). Data warehousing, Extract Transform and Load (ETL), Business Intelligence (BI) are key information technologies concepts derived for decisionmaking (Figure 2). Inmon (2005) defined data warehouse is a historical, subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's INTRODUCTION 4 decision making process. Various authors like Vijayendra and Meiliu (2013) defined ETL is a process to extract data from diverse data sources, transform the extracted data as per the operational needs and load the transformed data in data warehouse. BI is defined as technology for data gathering, storing, analysing and dissemination of the information for timely and fact-based decisions (Barone et al. 2010). BI functionality broadly includes the activities of decision support systems, query and reporting, Online Analytical Processing (OLAP), statistical analysis, forecasting, and data mining (Ciobanu et al. 2010). Inatlas 1 is a company based in Barcelona which is trying to analyze and categorical present all the business data of Spain to its stakeholders. However the company is still evolving to efficiently handle the big data and translate the data into meaningful information. Translating the analyzed results into something the science and business community can understand is challenging. This can be achieved by imbuing BI functions in any decision-making ecosystem. DSS based on BI functions such as data aggregation, large-scale integration, and analytical capabilities enables data trends, quick computation of complex views facilities business managers to reach a decision (Tong et al. 2008). Thus many management decisions and scientific analysis can be significantly influenced by DSS which is based on BI functions as this strategically analyses the profiles for guiding decision-making. The adoption of BI functions to the spatial domain yields interesting results for decisionmakers (Bédard et al. 2001). On a fundamental level, location based information technology; identifying spatiotemporal patterns are compelling assets for most of the projects. For example, there will be several scientific and business factors that need to be considered in setting up renewable energy plants. In a given area, it is necessary to delve for factors such as best energy source, site selection, operation and maintenance costs. On other hand, scientific analysis like the areas with a good amount of solar radiation, average wind speeds, etc are location and time dependent. Martinot (2002) and Twiddle and Weir (2006) highlighted inappropriate selection of location and technical risks of wind farms that are damaging the nature around the wind farms and vice versa. Thereby, before venturing into the projects location based information technology is vital to study the ground truth and for further investments. As per Facebook Chief Operating Officer Sheryl Sandberg “Local is huge, it’s the holy grail of the Internet. The problem with local businesses is that they’re not very tech savvy. Something like 40% of them have no web presence at all.” (Techcrunch 2012). Therefore breakthrough developments in geospatial technologies and the increasing availability of Spatial Data Infrastructures (SDI) make geoinformation a business and a decisional element to the management (Bejar et.al 2012). Hence, it is important to have a management plan to factor in practical and feasible data sources, in building geo application for various application scenarios. Rivest et.al (2003) aforesaid that geo applications are not well developed for decision making as they are often used at the operational level. BI functions like OLAP, which is a very popular category of decision-making tools, have facilities for interactive and intuitive exploration and analysis of data with multi view capabilities (Bédard et al 2001). To properly understand technologies rudiments (BI, Data warehouse, ETL) for both business and scientific data analysis, one must start with the right kind of conception. These technologies leverages to answer questions like "Would this technology help me to make my analysis fast to socialize 1 http://www.inatlas.com/en/about_inAtlas 11 INTRODUCTION Problem specification: After the analysis stage, this fends foundation for the motivation and challenges that need to be addressed in this line of research. Conceptualisation: Eclectic approach is proposed to the challenge identified during the conceptualisation stage. Implementation: The challenge identified and the approach proposed is vast and complex. To give a tangible benefit of the conceptualized architecture led to implementation and development of a web application for exploring solar radiation. Evaluation: The web application developed was submitted to an International Space Apps contest conducted by National Aeronautics and Space Administration (NASA). This is a global competition where 9,000 people, 83 cities and 44 countries participated. The application was evaluated and recognized by a group of NASA scientists and other experts in renewable energy sector. On the other hand, the tool developed is qualitatively compared with the functionalities of other global energy atlas from International Renewable Energy Agency (IRENA) and National Renewable Energy Laboratory (NREL). Figure 4: Research methodology INTRODUCTION 12 This thesis attempts to propose DSS for effective use of renewable energy sources. In light of proposing technical solution, this work deals with various diverse subjects such as Information technology, Geoinformatics and Renewable energy. However, when time, expertise and resources are constraints, selection of technology and strategic execution must be done carefully to realize the potential gains. As can be infer from the Figure 4, each stage of the research plays vital role and are executed step-by-step. Essentially, it is classified in three main stages: Requirement analysis or product gap, Design solution and Evaluation. In the early stage of research (Stage 1 Chapter 1 & 2), a deep dive into the renewable energy theoretical concepts and existing models are made to determine the requirements between demand and supply on the current capabilities of the available models. Since business is about economics, calculations, time, profits and losses, this thesis also proposed a concept called Expeditious management plan for economical development of geo applications (Chapter 3). After the analysis of the product gap in renewable energy field, next important step is to identify the desired outcome and the process or technological approach to achieve the desired outcome. In this process, this thesis identifies capabilities of Data warehousing and BI that will provide the best process to fill the product gap. The technical advantages and capabilities offered by these concepts are laid while characterizing the REDSS (Chapter 4). Conceptual designs outlines desired features and layered architecture of the model. Based on the cross-domain technology, a conceptual model (Stage2, Chapter 4) was developed to address the product gap identified in Stage 1. In addition, various uses cases of the model were identified and discussed in detail. Combining the concepts, user requirements, functionality gap into a prototype (Stage 3, chapter 5) was followed by qualitative assessment of the prototype compared to the other major global services. Prototyping involves implementation of conceptual design to one of the use cases highlighted in the thesis. The qualitative evaluation is based on a set of tool performance evaluation parameters and served to: Identify potential product gains in terms of tool performance and user experience with the new design solution and developed prototype. Discover more interesting views and mining more information than other existing tools. Explore more on visualization capabilities for different sections of users. Generate more ideas for product improvement and development. Conceptual model, prototype and empirical evaluations allowed addressing research objective and research questions raised in the thesis. Since this research topic is vast and complex there is a great deal of scope for recommendations, addition of more product features and future work on the problem identified and user requirements (Chapter 6). 13 INTRODUCTION 1.6 Scope The research will provide DSS for renewable energy using BI functions and geo data models as shown in below conceptual Figure 5. However, most of the REDSS is based on theoretical account since the data models involved are diverse, expensive and have very limited access to freely available datasets. Similarly modelling diverse datasets are much complex, requires lot of man-hours and engineering efforts which is much beyond the scope and time of this thesis. Future work section in this thesis highlights various challenges, enhancements that can be done to REDSS. Each of the future work list highlighted has potential to be a thesis topic for engineering, post graduation and management studies. Broadly, below are some of the points that limit the scope of the research. Figure 5: Overall basic architecture of the REDSS Data models: As shown in Figure 5, REDSS holistic ecosystem depends on various models such as 3-dimensonal (3D) building models (a very detailed model which should also includes external projections of the building), high resolution Digital Elevation Models (DEM), some uses cases will also need other external data models like climate, solar, wind, vector data, textual files etc. Data quality: Quality Assurance (QA) and Quality Check (QC) on the in-house data models and other external data models are quite challenging and tedious. For example, to prepare very detailed 3D models, a very high resolution of satellite data and meticulous expert efforts are required. It is not only a tedious process but also very expensive to procure high-resolution satellite data. Imagine the expenses and human efforts required, if the 3D modelling has to be done for an entire city, a region or a country. Though there are other alternative economical methods but their quality results will not be a good match for some use cases like sun and shadow navigation services. However the Chapter 3 of the thesis proposes an expeditious management plan for developing low cost data models from free available data which may be “good-enough” for some projects. INTRODUCTION 14 Algorithms: Several algorithms and mathematical models need to be involved for complete REDSS model. To cast sun position, its movement at different places on earth at different times in a day requires sophisticated sun position algorithm. Shadow simulation with respect to sun position, location and objects is very challenging because it has to work with diverse models for expecting ground realistic results. QA and QC check mechanism on these algorithms, data models efficiency and accuracy in both manual and automatic mode need a complex software set up and expertise. Though there are good numbers of existing algorithms such as r.sun module from Geographic Resources Analysis Support System (GRASS) 10 software but the challenge still remains in customize and integrating the existing ones into one such complex ecosystem. Testing: Different uses cases may need different levels of data quality, simple to complex computing algorithms, database, graphical user interface and other software infrastructure. All these complex infrastructure needs to go through proper software and data testing mechanism for realistic outcome. Diverse concepts: REDSS is based on various concepts such as Renewable energy, Geoinformatics, Computer science and Business management. One who deals with REDSS complex model should be an expert who has experience in all the fields or group of people of varying expertise in each field. 1.7 Thesis structure The thesis is divided into several chapters and each chapter has significant and substantial contributions to the thesis as a whole (Figure 6). Each chapter aims to systematically answer each research question step by step and are organized as follows: Figure 6 : Abstract sequential view of the chapters. Chapter 1 (Analysis) elaborates about the thesis background, requirement analysis, problem statement and motivation behind the thesis. This chapter defines the objective of the research work, followed by methodology required to fulfil the research questions outlined. It also highlights the major contributions of the thesis to the renewable energy sector. 10 http://grass.osgeo.org/grass64/manuals/r.sun.html 15 INTRODUCTION Chapter 2 (Analysis) reviews and categorises the existing sun-shadow models for requirement analysis. Essentially this chapter is divided into 4 different sections Sun models, Shadow models, Sun-shadow models and spatial data and it sources. Finally, in the end of the chapter, the need to have a decision model for better understanding of renewable resources is explained. Chapter 3 (Analysis & Conceptualization) presents framework for extensive list of different methods and data sources that are available in the geo market. The central important part of the chapter is to illustrate in detail economical development of 3D data models after careful reviewing of the framework proposed. These 3D data models indeed will be used in future encompass out-and-out sun-shadow mapping. Chapter 4 (Conceptualization) presents the detailed architecture and application use case scenarios of REDSS. This chapter plays a fundamental role in the thesis as this is the gateway for the solution to the problem identified and the future work. First, characterizing of the model and significance of the each component of the REDSS is explained in detail. Further, it highlights several key points of the REDSS model and its importance in real world applications. Chapter 5 (Implementation & Evaluation) manifests the potential of the approach proposed in the previous chapter. A web application is designed and developed to show the solar radiation at different geographical areas, for different days, months and years. The web application hierarchical stores humongous historical data and presents the data on graphical formats such as maps, chart and tables. A brief qualitative assessment is made to test the distinguishable quality of the approach proposed with other global tools in the market. Finally, Chapter 6 concludes the thesis results, summarises the contributions and suggests important future recommendations. Chapter 2 STATE-OF-ART SUN-SHADOW MODELS One of the important goals of this chapter is to do a research on the existing approaches and solutions for sun-shadow infrastructure for analysing developing potentials. Sections (2.1 to 2.4) gave an overall idea required to introduce new product after reviewing existing models and market potential. As a supplementary to this chapter (Section 2.5 and 2.6) discuss about the type of solar radiation data sources, spatial data and its various data sources. Availability of right data sources, diverse and big data challenges, data quality and proper infrastructure to handle the data are major obstacles to most of projects. Hence, last section speaks about data and its sources which is the essence of any project or application. It focuses on the data sources and types that are particularly related to the thesis. After studying on the data challenges and product gap paved way to lay foundation for proposing and designing REDSS. Subsequently next chapter extensively tries addressing data making process by proposing a framework called “expeditious management plan”. Summary section concludes the chapter. 2.1 Introduction A preliminary assessment of the problem is discussed on bases of the Context, Model and Solution. This chapter discusses about the technical situation (Context), existing technologies (Model), data and a better approach (Solution) to address the research objective. Figure 7 plots the relationship between Model maturity and Context maturity to understand the problems. From the thesis point of view, Model maturity primarily focuses on three types of models: Sun, Shadow and Sun-Shadow models. Sun model shows the sun´s position and its intensity of solar radiation with respect to ground co-ordinates. Shadow model displays the shadow position with respect to sun position. Sun and shadow model forecast sun positions and casting shadow simulation for the ground objects. Although shadow and sun-shadow models are technical similar as in both cases casting of the shadows is based on sun position. However in sunshadow model, quality of data, diverse data models, sun position and shadow casting algorithms involved need to more mature and accurate to address more complex contexts. Functionalities and limitations define the maturity of the model required for particular context. For example some programming libraries such as SPA (Sun position Algorithm) from NREL 11 belong to Sun model but have potential to evolve and be adapted to Sun-shadow model. Context maturity: Schilit et al (1994) defined Context as “Knowledge about the user’s and IT device’s state, including surroundings, situation, and to a less extent, location”. Context is classified into four categories: Computing context, User context, Physical context and Time context (Chen and Kotz 2000). 11 http://rredc.nrel.gov/solar/codesandalgorithms/spa/ STATE-OF-ART SUN-SHADOW MODELS 18 Computing context: Network connectivity, Location, Data model and Communication. User context: User requirement, Location and Social status Physical context: Solar radiation, Wind speed and Temperature Time context: Time of the day, week, month, quarter, and year. The models stated above should be a good fit to the scenarios and the varying degree of scenarios as discussed below and use cases highlighted in this thesis. Each of the below use case scenarios requires varying degree of different categories of the Context. As shown in Figure 7, model maturity is directly proportional to context maturity. More the maturity of the model, more it is suitable for resolving complex contexts. Figure 7: Context maturity verses Model maturity 1. What is the sun position at these co-ordinates at this particular time? Resource: Box 1; Use case: fishing 2. What is the sun position during 9 A.M to 12 P.M in January verses August? Resource: Box 4; Use case: Viticulture 3. What is the shadow position of these co-ordinates at 3 P.M? Resource: Box 2; Use case: shaded cafe 4. What part of this building shadow overlap with neighbouring parking lot? How long? Resource: Box 5; Use case: site suitability e.g. solar parking 5. Which is the shade path and best time to visit the city? Resource: Box 3; Use case: Sun-shadow navigation services. 19 STATE-OF-ART SUN-SHADOW MODELS 6. Is this portion of the building suitable for smart windows and building integrating photovoltaic? Is it free from shadow effects of surrounding objects? Resource: Box 6; Use case: Solar cells for buildings. Figure 8: DSS as a solution for model and context maturity Solution: Different contexts have different requirements and require different functionalities. To propose a solution it is necessary to understand the nature of the context and architectural support of the model to solve the purpose. Thus a good framework is important to establish and understand harmonious relation between context and model. Most of the existing models, data libraries and algorithms explained in next section fall into the lighter tone of the boxes (Figure 8). DSS framework for spatiotemporal analysis for real world understanding of challenges and scenarios is necessary as a Solution to cover and address various Contexts discussed in this thesis and as shown in Figure 9. STATE-OF-ART SUN-SHADOW MODELS 20 Figure 9: Importance of DSS for various sun-shadow use cases (Inspired from GoGeomatics 2013). 2.2 Sun models Challenges Sun path temporal fragmentation per the user’s choice and events is one of the primary challenges in sun models. Also, on the fly graphical breakdown of the Sun’s path and also its radiation data based on the timeline and time segments to map various temporal events in a day are some of the challenges identified in this model. Techniques There are different interesting and influential sun position algorithms such as Spencer, SPA, Vant-Hull etc. These algorithms are either directly implemented or developed into the models to calculate the Sun’s path based on the Sun Azimuth and Altitude, Date, Time and Location. Blanco-Muriel et al. (2001) proposes PSA algorithm for sun position and compares with various existing algorithms: Spencer, Pitmann, Vant-Hull, Walraven and Michalsky. Based on their analysis, PSA algorithm is simpler and easier to compute and track the sun position with higher accuracy. Chapter 3 DECISION MAKING ECOSYSTEM This chapter presents Expeditious management plan, Workflow and Pragmatic concepts (Section 3.1, 3.2 & 3.3). These concepts help to identify required data sources and define workflow to choice best data making approach in creating cost-effective geo models. As discussed in the Scope section of the introduction chapter, data quality and right data sources limits the scope of the project. In order to resolve the problem to some extent, this chapter presents the Data source framework (Section 3.4). This framework highlights the sources and methodology from creating new data to spatial data sharing methods. Based on the management concepts proposed and data sources framework, development of 3D digital model of Zaragoza city (Section 3.5) is presented. Finally, the 3D digital model of Zaragoza city will be used for various applications use cases in general and renewable energy studies in particular. 3.1 General scheme for expeditious management plan This section identifies a general management pattern for choosing a better data source taking into account several aspects related with the objectives of the project used as an application example in this chapter. A plan is a set of sequential steps along with the resources required to accomplish an objective (Washington and Lees 2004). Further, a contextual concept called pattern would also be a valuable and highly complementary to the plan concept. Martin (1997) provides an insightful definition of a pattern “An idea that has been useful in one practical context and will probably be useful in others”. Apart from solving specific problems, patterns are also used in providing architectural outlines that may be reused in the development process of a program (Gamma et al. 1995). The expeditious management plan described is the result of deriving and incorporating plan and pattern concepts into geo-management for fast, efficient and an economical way of building geo applications. This section discusses a solution in geo project management by structuring it into three rudimentary components (Figure 10): Workflow management, Pragmatic process and Data sources framework. DECISION MAKING ECOSYSTEM 28 Figure 10: General scheme for expeditious management plan 3.2 Workflow management The workflow management is based on the classic waterfall life cycle model used in Software Engineering. The basic steps are: Requirements analysis. It is necessary to review the requirements of a project before its inception. This step analyses the project proposal in order to determine the data necessities for the project, besides the Quality and quantity that the project requires. Matching necessities and availability. The next step is to determine how a manager can match the necessities with the available resources. In this way, it would be necessary to review the accessible “markets” looking for the resources available. This “market” will also be related with the requirements of the project. It will be necessary to take into account our necessities and the monetary resources the project has. As a 29 DECISION MAKING ECOSYSTEM result, a Framework path will be selected in order to determine the data resources that will be used, and the data transformation processes to be applied. This Framework path is a part of the Pragmatic process and Data sources framework explained in the later sections. Data construction and/or acquisition. By applying resource selection and transformation processes established by the Framework path, the Workflow management will provide a Data source proposal to be used for achieving the objectives of the project. Quality and Quantity check. Finally, before providing the data for the project, it will be necessary to check the proposal in order to fulfil the Quality and quantity that the project requires. 3.3 Pragmatic process This process aids in selecting a data source that is minimalist, beneficial and useful to the project. It is based on the following components: Problem: Extending the software management viewpoint to Geo application leads to identification of the problem which is in two folds: (1) Context: The context in which the application has to be developed is based on its objective and factors such as quality of data, method of acquiring the data, timeframe, financial and human resources available. As per our application objective, we develop 3D city models by reducing the process cost in terms of data acquisition time and cost of the data. (2) Forces: Constraints, like the availability of data, time, it’s fitness for use, technology support, and costs to procure the data are some of the forces or setbacks in an application development. Such forces in our project are the unavailability of LIDAR data as per the project schedule, study area and the price of data. Based on the definition of the problem the data sources can be selected. We found a way to achieve the objective of our application through the mix and match approach as described in the sections below: Data sources: Based on the problem identified, we present different approaches in obtaining the data for building the digital earth below: (1) Accessing the external datasets without hassles of creating and maintaining the data by exploring readymade data using spatial portals, web services or other data sharing technologies. For example, consider the Environmental mapping case discussed in the above section. (2) Using the existing readymade resources as base data can be made compatible by deciding on the right mix of services and tools. For instance, NATO operations in DECISION MAKING ECOSYSTEM 30 Libya 15 or Hurricane application 16 build spatial data from one source as base data and non-spatial data from other sources. Online tools like GeoCommons 17 where users can make an analysis and visualize maps of one’s own data, various available datasets and base maps. It also has a provision to download or integrate on the websites. Likewise, in portals like Map tools 18 programmers and users can find various open source web tools, desktop tools, source codes and many other potential resources that come handy in during building of applications. (3) Choosing economically data collection techniques as compiled by the respective authors. For example, (Table 3): portrays various 3D optical acquisition methods based on the application requirements, (Table 4): shows the most suitable data methods for various application domains. However, they may not be copperbottomed but they give an approximate idea. (4) Mix and match approaches: The application objective can also be achieved by making use of the benefits of any or all three approaches stated above. For instance, the Geo marketing case explained in our earlier section or the concept behind the Google Earth builder 19 . This approach is named as Mix-andmatch and explained in detail in the next section. Number Application requirements Suggested techniques 1 3D data accuracy P 2 3D data details LS 3 Fully automated 3D data retrieval 3DCR/LS 4 Nighttime operation 3DCR/LS 5 Low equipment cost P/V/3DCR 6 Portability P/V/3DCR 7 Long measurement range LS Note: P=photogrammetry; V=videogrammetry; 3DCR= 3D camera ranging; and LS= Laser scanning. Table 3: Suggested technology for 3D data based on the application requirements (Zhenhua and Ioannis 2009) 15 http://www.guardian.co.uk/news/datablog/interactive/2011/may/23/libyanatobombingnoflyz one 16 http://crisislanding.appspot.com/ 17 http:// geocommons.com/ 18 http://www.maptools.org/ 19 http://www.google.com/enterprise/earthmaps/builder.html 31 DECISION MAKING ECOSYSTEM Table 4: Data sources and various application areas (Omar and Ayhan 2009) 3.4 Data sources framework This Framework has been structured around the research on the optimized data sources to get the desired readymade data or to customize already existing data for the project. In this process six basic elements have been identified: (1) Remote Sensing: Remote sensing is the science to sense and record information of a remote object or area without any physical contact. Khagendra and Robert (1991) state that this technology offers us a wide range of data sources from low resolution to high-resolution satellite images. Studies such as Cary (2009) say that optimal technologies like the LIDAR provide point data, which is used for widely used in many building roof and terrain modelling (Verma et.al 2006). Undoubtedly, this data source provides the best results. Nevertheless, factors like the cost involved, unavailability of data for our study area and requirement of specialized software and hardware to handle the enormous data were a setback. (2) Photogrammetry: Photogrammetry is a scientific technique to measure and determine the properties of the object, and to add 3D dimensional (height) coordinates to the 2D objects. The studies of Zhenhua and Ioannis (2009) and NCHRP (2003) state that the data obtained by the Photogrammetry methods are accurate, updated and flexible. These characteristics of a data source provide a good option in achieving the objective. Nevertheless, complimentary web service for accessing orthophotos could be a better option to ease the project resources. (3) Digitization: Digitization is the process of converting raster data (satellite/aerial images) to vector data (line, points, polygons) for mapping and other ground truth activities. The NCHRP (2003) report explains that this technique gives an accuracy of five to 50 feet; it is less expensive and faster than other conventional methods. Converting the paper maps to spatial data using automatic, semi automatic and DECISION MAKING ECOSYSTEM 32 manual methods, can be an option for some applications. As mentioned in the above section, the advent of specialized software such as R2V 20 and WinTopo 21 saves a lot of human effort and time. As a secondary option, this could be combined to the photogrammetric technique to obtain the plain metric and height data. (4) Surveying: Surveying is the technique to measure horizontal distances, height, direction and angles of the physical features of the Earth. The NCHRP (2003) report says that, this approach could be beneficial for projects limited to smaller study areas or when the data needs to be updated. Surveying element gives highly accurate data (it is the best fit for urban application). (5) Spatial data sharing: The fundamental principle behind the concepts: spatial data infrastructure, open source, interoperability, web services fosters the idea of sharing data. Shared data can be used by different people for a multitude of applications scenarios (GSDI 2011). This not only saves time, money and human resources but also avoids the duplication effort involved in creating and managing data. Spatial portals provide a platform in finding a dataset in a faster and efficient fashion (Winnie and Jan 2005). All these concepts aid in discovering and integrating the apt data sources for the quick building of applications. (6) Mix and match approaches: Google Earth builder 22 uses this approach to upload and manage data from various sources to form a custom map. This is a good example of the mix and match approach. Creating new data would exhaust resources. As highlighted earlier, the discovery of the data that fits or base data that is close to the objective can be tailored by using any of the methods mentioned above to make it fit for the purpose of the project. For example, the case wherein, if the base data that is discovered through spatial portals or any other sources is incomplete to meet the application objectives. This can be completed or improved by discovering related web services, right mix of web services, spending minimal resources in field survey on the pivotal area of our study for updating or enhancing or purchasing the data component that is required to complete the dataset. In this way instead of preparing the data from scratch, a mix-and-match approach may subdue project resources. Empirically, this approach saves resources and boosts efficiency. The 3D model that is developed in next section shows the 2D cadastral data discovered via web services used as base data, which is eventually enhanced to generate a 3D model. 20 http://www.ablesw.com/r2v/ 21 http://wintopo.com/ 22 http://www.google.com/enterprise/earthmaps/builder.html 33 DECISION MAKING ECOSYSTEM 3.5 Digital earth 3D city model The pattern presented in the previous section has been used to obtain a 3D model of the Zaragoza city, Spain. This 3D model is going to be used in the development of a project that has as its main objective: the construction of the 3D information that will represent the shadows of the city at different times in a day, and different days in a year. This information will be used in different domains such as in the models of which analysis the spread of pollution in the air (that are affected by differences in temperature), selection of the location of solar panels on buildings, or tourist routes (different tourist routes depending on the season and time of the day). The Workflow management proposal has been applied to this particular problem by developing the task presented in the following steps. 3.5.1 Requirements analysis As mentioned earlier, the 3D model can be used in the development of a project that has to provide 3D information to represent the shadows of the city at different times in a day, and different days in a year, potential location for installation of PV etc. This information is not going to be used directly for visualization, so its adjustment to the real appearance is not a requirement. In addition, the relevant information that the project requires is the volume of each building (height, width and length). The granularity has to be at the building level because it will be necessary to analyse the building connections and dependencies. 3.5.2 Matching necessities and availability In order to select the Data sources, experiment and discover the source using the Mix-andmatch element of the Data source framework. Cartociudad service is used to get the number of floors of the buildings and to obtain the coordinates of each building (Cartociudad 2010). Nevertheless, the problem with this approach was that the accuracy of the information provided by Cartociudad is not homogeneous and does not have the same quality in all geographic areas, which eventually did not give the desired accuracy for use in an urban setting. Revamping this approach by analysis of various existing services like the OpenStreetMap, Cartociudad and Catastro, the Cadastre WMS (Catastro 2010) was selected: Catastro results were closer to the ground truth and gave an uniform quality of data necessary for the urban environment. 3.5.3 Data construction and/or acquisition The 3D model for Spanish urban areas of Zaragoza has been sequential developed as shown in the flow diagram (Figure 11) and as the steps described below: Extraction: The map data of Zaragoza city was procured by making requests to the Cadastre database by OGC WMS service for buildings (CONSTRU) and building with labels (TXTCONSTRU) layers, which contains necessary information for the 3D modelling. DECISION MAKING ECOSYSTEM 34 Transformation: The data transformation or enhancing the data from 2D to 3D is done by vectorizing the building parcels that provides 2D data, by applying OCR algorithms for recognizing labels that contains height values, and by performing a spatial join between building shapes and georeferenced height values for obtaining 3D buildings. CONSTRU layer which renders the geometry of the building parcels, and the TXTCONSTRU layer which contains both the geometry of the building parcels and textual numerical values are in raster format. In order to get the height information (TXT), a map subtraction operation is performed between the buildings with labels layer and the buildings layer. Then, an OCR algorithm is applied on the result of the subtraction to read and calculate the numerical values of each building parcel to get the number of floors and to estimate the height of the building. Finally, the vectorized buildings layer and the georeferenced heights are spatially joined for obtaining 3D model of Zaragoza city. Figure 11: Illustrates the stages involved in making the digital earth model The time taken to build on the town of Zaragoza, covering an area of one square km and a resolution of 0.5 meters stored in a file in ASCIIGrid format of 8,030 KB is eight hours. The whole process has been set up on the Ubuntu 9.04 operating machine. GRASS and the Java programming language have been used to implement some of the specific processes that were required for this experiment. There are also a few drawbacks to this approach: The process time is high, as all operations had to be performed sequentially. Part of the process relies on Cadastre WMS, on which there is no control because sometimes the service may be unavailable or may return incomplete responses during the data acquisition. Thus, during some stages like eliminating duplicate polygons, dangles etc human intervention for the quality check is needed. Apparently, the Digital Terrain Model may not be as accurate as produced from conventional methods. Despite the setbacks in this approach: this is resources saving, interesting and may bear fruit to our application. This demonstration also highlights the data value improvements and the substantial usage of exiting data. 35 DECISION MAKING ECOSYSTEM 3.5.4 Quality and Quantity check The revision of the data generated in the previous step satisfies the requirements for the project and as presented in the section Requirements analysis: The information created can be represented graphically by “overlaying” an aerial photograph over the structures created (Figure 12). However, it is not an aesthetic view of the city, but could be enough according to the project requirements. The information has been built as a raster file that represents a digital terrain model that which includes the buildings. With this information, it is possible to know the volume of each building (height, width and length). Figure 12: 3D model of the Zaragoza city The above 3D city model has been briefly compared with an analogous 3D representation based on LIDAR data. In this way, the free LIDAR data provided by the Gipuzkoa province in Spain (Gipuzkoa 2011) is used for this task. The LIDAR data of grid one square km was processed in an hour by a computer machine. Likewise, the use of our approach on the same area grid has taken five hours. The time taken to process the LIDAR data is exceptionally fast. Nonetheless, considering parameters like the availability of data, cost and time involved in procuring the data, expertise, the software and hardware infrastructure required is much more demanding. Apparently, LIDAR data gives a very high quality and is dense however, a good enough solution like the one demonstrated in this paper will be sufficient for some digital earth applications. DECISION MAKING ECOSYSTEM 36 3.6 Summary This chapter emphasized on expeditious plan and the usage of existing potential sources and the benefits outweighing creation of a new dataset. To postulate the theory proposed, low cost development of virtual 3D city model of Zaragoza city is demonstrated. In overall, this chapter proposes a framework that allows a technical and managerial team to choice the best data source for creating cost-effective geo models. However, there are a few glitches and legal issues if the applications are developed for some commercial purpose when using some services like the Google Maps service. Each approach discussed in the above sections could be appropriate depending on the modelling context and forces. This chapter also proposed some management concepts in relation to choosing data sources that could be directly integrated into Geo environment and could be enough for the benefit of the public and business markets. 43 RENEWABLE ENERGY DECISION SUPPORT SYSTEM (REDSS) extracted from external sources and stored in in-house data warehouse has been significantly used in developing the web application and 3D models in this thesis. 4.2.2 Sun cube It is the extension of the OLAP cube used in the data warehousing concepts, which is used for analysing the business data. Sun cube based on the concepts of OLAP is used to interactively analyse the solar energy pattern by roll-up, drill down operations of an OLAP cube. In principle, the Sun cube consists of four dimensions: Sun position (sun pathsun segment-sun point), Data (Area-3Dmodel-point), Time (yearly-quarterly/seasonal-monthly-weekly-daily-hourly) and Ground location (county-province-city-building/co-ordinates) to analysis the data for various applications and detailed reports generated by Shadow cube (Figure 15). Sun cube also hierarchically structures and present the solar radiation data both on map and charts. Energy2People is one such application, which is designed to store and present the huge amount of solar radiation data derived from NASA. Technical details on the Energy2People are discussed in Chapter 5 of this thesis. 4.2.3 Shadow cube In line with Sun cube, Shadow cube is also based on the concepts of OLAP. These BI technologies will analyse the data structured in the Sun and shadow cubes to answer various use cases scenarios like the questions listed above. Shadow cube for shadow simulation takes the input from the Sun cube for sun position and the 3D data models plus DTM as ground data for analysing and generating the hierarchically reports in all the dimensions as shown in the star schema of the Shadow cube (Figure 16). The expected output of this cube is shadow maps of an object, a location and a region. In analogous to topology which is of spatial relation of the geometric objects, shadow topology maps should also reveal spatial relations of shadows. 4.2.4 Solar reports These are hierarchical reports in the form of chat, tables and maps based on the concepts of Presentation layer of BI. In this study, along with the graphical interfaces, spatial interface is also considered for more realistic sun-shadow data analysis. These reports will be structured report based on the solar ranking system. Solar ranking for the ground locations are evaluated based on the two criteria's, one is the time period for which the sunlight available and intensity of solar radiation. Reports are presented hierarchically (country, province, city, coordinates) and time wise (yearly, seasonal, quarterly, monthly, daily and hourly). Based on the processed inputs from Sun, Shadow cube components reports are generated. Sun and shadow reports helps the management team to analysis the ground truth intuitively for quick and apt decisions in prioritizing and choosing the city, site location etc of the solar projects in Business Process Management (BPM), forecasting, budgeting, management of reports etc. RENEWABLE ENERGY DECISION SUPPORT SYSTEM (REDSS) 44 Dim_Time ID Year Quater Month Day Segment Hour Dim_Data ID Area 3D model Point Dim_Sun position ID Sun path Sun segment Sun point Dim_Ground location ID Country Provience City Building Fact_Shadow Time ID Ground location ID Data ID Sun position ID Properties_Shadow Figure 16: Star schema for Shadow cube 4.3 Users REDSS gathers and analyse information regarding location and their renewable energy characteristics, in order to develop deeper and more effective strategic decision-making. Target users for REDSS is essentially divided into three categories: Expert users like architects, agriculturists etc, Novice users that refers to general public and, Computer systems, as this application can be integrated with other applications. REDSS mechanism is designed to address various ranges of cases scenarios such as scenario-based maintainability assessment, situational awareness, impact analysis, comparative studies, and navigation services. REDSS provides platform to view and analysis data from global perspective to local perspective. It provides provision to interact and chose the hierarchy of information layers as one navigates the hierarchy. The limited technical knowledge of general public or senior management can be addressed by interactive charts and reports. As these reports cognitively aids all sections of users to understand the information easy and clearly. The intuitive hierarchical decomposition of information provides highly valuable input to the identification and selection of location. 45 RENEWABLE ENERGY DECISION SUPPORT SYSTEM (REDSS) Figure 17: User maturity verses Data maturity As shown in Figure 17, REDSS primarily concerns two different dimensions: User maturity and Data maturity. Based on the technical level of understanding and requirements, User maturity deals with three kinds of users such as Novice users, Computers and Experts. Different projects have different data requirements depending on budget, quality, quantity, availability and ease of use. Based on these factors, Data maturity is categorised into two different levels: High and Low. Novice: 1. Question: What is the available solar radiation for my province? Resource: Box 1. 2. Question: What is the solar radiation available for my roof top? Best position to install PV plant? Resource: Box 4 Computer: 1. Question: What is the weather report and best time to fish for tomorrow at this location? Resource: Box 2 2. Question: What are popular tourist’s spots in the city and sun path to visit the all the spots? Resource: Box 5 RENEWABLE ENERGY DECISION SUPPORT SYSTEM (REDSS) 46 Expert: 1. Question: What is the solar radiation for Spain in year 2000 verses 2011? Resource: Box 3 2. Question: Why is there an uptick in solar radiation and downtick in wind speed in January 1999 verses January 2011 at this particular location? Resource: Box 6 REDSS has potential to adapt to other renewable energy sources like wind, biomass, geo thermal etc. For example, below Figure 18 shows schema for Wind atlas to identify best location for setting up wind farms. Careful selection of site selection should avoid some of the problems highlighted by Martinot (2002) and Twiddle and Weir (2006). Similar approach will be very resourceful while for planning for combination of solar (PV plants) and wind energy (wind farms) as shown in Venn diagram (Figure 19). As these two renewable energies acts as complementary sources, scientifically this due to solar radiation is usually high when wind is less and vice-versa (Maaßen et.al 2011). Dim_Time ID Year Quater Month Day Segment Hour Dim_Height ID 10 m 30 m 50 m 80 m Dim_Wind power class ID 1 2 3 4 5 6 7 Dim_Location ID Country Provience City Offshore Onshore Fact_Wind atlas Time ID Location ID Height ID Wind power class ID Properties_Wind Figure 18: Star schema for Wind atlas 47 RENEWABLE ENERGY DECISION SUPPORT SYSTEM (REDSS) Figure 19: Venn diagram for Wind and Solar energy location. 4.4 Unique points about the model proposed 4.4.1 Eclectic approach The model introduces the benefits of non-spatial technologies like BI to gain better understanding of the spatial domain. Concepts like the Sun cube, Shadow cube: shadow topology, shadow maps, solar ranking, interactive and detailed reports using drill down and roll up operations are derived from BI to help us to understand and plan the complex spatial problems in a comprehensive way. Further, this model analyse location based historical data, which will be useful for studies like global dimming and brightening. 4.4.2 Layered structure and intuitive reports This is the key module in the model for disseminating situational awareness, which is useful for experts, novice users and computer machines. The intuitive drill down reports give a very quick insight along the sun position, time, data and ground location by just click of a mouse. The detailed reports which are just numbers derived from the user-preferred combination based on application requirements can be easily elucidated by novice users. Depending on the project requirements, the layered structuring can be easily integrated with the other external system as part of the database or as a database whole. RENEWABLE ENERGY DECISION SUPPORT SYSTEM (REDSS) 48 4.4.3 Diverse applications Solar energy can be widely used as either main energy or additional source of energy in various fields. Identifying the solar energy needs in various diverse existing applications to the latest developments either as a subpart or as an integral part of the application. 4.4.4 Co-creation solution Interacting and innovate with the domain expertise on the feasibility of this model in enhancing the product. For example, this model can be vital for the transportation sectors (sun and the shade path for solar vehicles and tourists), electric car charging stations powered by solar panels, right time and best location for film/photo shoot, Fishing: source location and time, Optical dating, weather information system etc. 4.4.5 Mix-and-Match approach Envisaging the possibility of this model not just as a stand-alone domain application but also by integrating with other applications like weather information application (sun geo position+ weather information+ local time), fishing, tourism application (sun and shadow route), as a subpart of the applications for enhancing and to give users rich application experience. 4.5 Emerging sun-shadow application areas Below are presented some of the emerging and interesting areas in the solar energy sector that need significant technical attention to yield the best outcome. 4.5.1 Solar power grid Demand for power supply has substantially increased due to the growing number of consumers and shortage of resources to generate adequate power. Conventional sources of energy like coal and petroleum are getting exhausted and thus leading to an imbalance ratio of demand to supply. Solar power grid reduces dependency on the conventional sources of energy, usage of free available sun energy for uninterrupted power supply, reduces the inefficient usage of power like theft of power, less vulnerable to unfavourable climatic conditions, which interrupts the power supply. Solar panels can also be a standalone set up especially in remote and rural areas of emerging nations individually without the need to connect them to the state/national electricity grids, which reduces the transmission and infrastructure costs. 4.5.2 Global warming Scant of fossil fuels to meet the power and fuel demands of the consumer, the burning of these fossil fuels is also responsible for global warming by releasing greenhouse gases into the atmosphere. So, a study on the Solar grids for electricity as stated above and electric cars that work on solar energy as explained below will help to reduce the global warming for a better living. 49 RENEWABLE ENERGY DECISION SUPPORT SYSTEM (REDSS) 4.5.3 Electric cars charging station Electric cars that run on electricity are generated from solar panels. They can be recharged at parking areas, at any business areas or we can also set up a personal recharge unit at home. Unlike the conventional fuel cars, they do not release any harmful gases into the atmosphere. 4.5.4 Greenhouse crop In the study led by Yao and Su (2011), parameters like the solar radiation data, geographic location and sunshine hours are used to calculate the average daily global solar radiation, which is required for the greenhouse crops like spinach, lettuce, greenhouse tomatoes etc. The authors also show the spatial distribution of global source energy in China for the greenhouse crop production. 4.5.5 Solar energy for architectures and designers Sattrup, PA and Strømann-Andersen (2011) investigated the effect of temporal and spatial dimensions of solar access in the urban environment. Calculating and mapping the impact of thermal energy performance is based on the orientation of the building, window size, structure and material properties of the building. 4.6 Spatial Business Intelligence for emerging application areas BI tools are designed to combine operational data with analytical tools in a flexible manner to communicate analysed business information to planners and decision makers (Negash 2004). Reporting, dashboards, scoreboards, OLAP, data mining, process mining, business performance management analytics and predictive analytics are the functions of the BI (OGC 2012). There are several essential components of proactive BI. One of it is the Geoinformatics (Langseth and Vivatrat 2003). Applying BI analytical and structured functions to the spatial data for the spatial analytical solution is Spatial BI. There are various emerging spatial markets such as public safety, health care, weather etc. that take advantage of BI for analysing the spatial and business data. Hence, this study proposes implementation of Spatial BI to deal with the key challenges presented in the previous chapters and for the use cases proposed below. 4.7 Value propositions for sun shadow use cases There is complexity and difficulty involved in calculating shadows in extensive urban areas because of the undulations of the Earth's surface, complex roof design and high-rise neighbouring areas (Seong et al. 2006). However, the advent and development in 3D modelling, LIDAR data etc. are widely used in many applications to collect and provide ground truth results for a better and effective solution. To select an economical spatial data solution is again challenging. There are various methods like Mobile mapping, GPS/total station surveying, Photogrammetric, Laser scanning or any other feasible source like the cadastral data. The method is usually selected based on factors such as the availability of required data, RENEWABLE ENERGY DECISION SUPPORT SYSTEM (REDSS) 50 data size and type, cost, time involved in procuring the data, as well as the quality and purpose of the application. On the other hand, there are substantial benefits that can be achieved through research in designing real time sun-shadow applications. These applications need some add on, integrated, updated, reliable models like the weather, cloud, soil and other seasonal data for out and out real time results in applications like carbon dating, fishing, viticulture etc. Below are some of the use cases that are identified, which may be useful for different user’s interest and benefits. 4.7.1 Tourist maps The G20 (2012) recognizes for the first time Travel and Tourism as a key and vital player in the development and economic recovery of the nation. Essentially, these maps show all the tourist destinations around the user location and the path to reach the destinations. It guides and navigates the users to the tourist locations along with the shortest and Sun-shadow navigation services from a user’s location to the destination. Sun/Shadow path meandering depends on the user’s choice of path and destination. Users will have choice to select two paths: Sun path or shadow path depending on the user's interest, time and seasons. Based on the ground and building DEM, the model proposed generates the sun path where users want to enjoy the sunlight. Likewise, it also shows the shadow path based on the user-preferred timings, start and end points. It can also be used to spot nearby shade parking lots, sunny/shade cafe restaurant based on the user's choice. All the models will have the provision to give the user feedback, which help in improving the model. 4.7.2 Sun-shadow navigation services As mentioned above, Sunshadow paths can be used as a navigation source for solar vehicles powered by solar energy. Sun path meandering, for example, if a person wants to travel during the daytime from Barcelona to Madrid, user can reserve the (Sun/shade) seat based on the sun shadow information available along the route. 4.7.3 Plant studies Solar radiation plays a vital role for plants healthy growth and yield. Shade plants like tea, coffee plantation or plants that grow from their leaves or roots such as chard do not require much sunlight. On the other hand, Sun-demanding plants like rice need more exposure to the sunlight and this should not be hindered by the neighbouring plants or other features like hills, trees or shrubs. Viticulture where topography and sunlight plays a predominant role for grape yards, this model can be used for sun plants, shade plant, carbon dating and greenhouse crop location studies based on the number of sunshine hours and the geographic location. 4.7.4 Architects and Urban planners UCLA (2012) researchers led a study on the development of solar cells for windows to generate electricity. The model proposed will aid the Architectures and Urban planners for 51 RENEWABLE ENERGY DECISION SUPPORT SYSTEM (REDSS) effective use of the solar cells for smart windows and building integrated photovoltaic, as this model provides the height of the surrounding buildings/ground features, shadow simulation maps, 3D shadow maps, shadow topology and solar radiation available in site area by intuitive comprehensive reports. 4.7.5 Site suitability The decision model determines optimized location for various applications like carbon credits, film/photo shoot, fishing and viticulture. Furthermore for solar panel performance analysis and installation for solar: cities, power plants, water distillation plant, pumps for farmers, power for metro stations, rooftop solar panels, mobile charging stations etc. It can also be used to compare the performance of the solar systems at different geographical regions and analyse the reasons behind the differences in performances. The model takes various ground parameters like the DEM (ground and building), sunshine hours and days, height of the source building with respect to the neighbouring buildings or any other structures like the hills into consideration to get unhindered sunshine for the solar panels to generate maximum output. 4.8 Summary This chapter presents the entire ecosystem of REDSS and its various application scenarios and, various kinds of actors who should get benefited by REDSS. REDSS is designed and developed, keeping in mind, different sections of users from general public to experts. In this line of research, a web service called Energy2people has been developed as a REDSS prototype for validating the feasibility of this approach. The architecture and characteristics of this REDSS application will be presented in the next chapter. By introducing BI concepts to renewable energy field, this research opens different way of structuring, organizing and presenting the information. In addition, this thesis works also focused on application use cases and envisaging new avenues that could be beneficial to the society. 59 DESIGN AND IMPLEMENTATION Figure 25: Interpolation process class diagram. In conclusion, this design will provide the configurability achieved in the ETL processes. What it means that a person without programming knowledge would be able to change Interpolation behaviour without “touching” any code. 5.3.3 Future work in ETL This section describes ETL process that do not appear in final system functionality, but that is designed and implemented during the last phase of the project. But some changes must be done in order to improve the performance. So, in this phase, it is tried to use a data warehouse with a native spatial support. For example, with this new approach, some spatial files such as ESRI-shapefiles, Geography Markup Language (GML), and Keyhole Markup Language (KML) etc can be stored in the SDW. Figure 26 illustrates the extraction, transformation and loading of data from ESRI Shapefiles sources to SDW. The ESRI-Shapefile, or simply a shapefile, is a popular geospatial vector data format for geographic information system software from Environmental Systems Research Institute (ESRI) 26 . Shapefiles spatially describe vector features: points, lines, and polygons representing, for example, water wells, rivers, roads and lakes. Each item usually have associated non-spatial attributes such as name, classification type etc. These Shapefiles can contain data that is not relevant in final application, so before loading data in the SDW, these fields are removed in the extract/transform process. 26 http://www.esri.com/software/cityengine/features DESIGN AND IMPLEMENTATION 60 Figure 26: ETL using ESRI-Shapefiles. Once spatial data is load in SDW, new operations can be executed. These operations, for example, can be used to separate data from different administrative units into data marts where data can be analysed faster because of its smaller size. Figure 27: Application example: Zaragoza. Figure 27 shows the change between data models (SDW model and data mart model) to achieve a more powerful multi-scale analysis. Taking advantage of administrative names stored in SDW, the data marts can contain only data from a particular province, region or city. This last approach maximizes possibilities to speed-up the analysis depending on the zone displayed on the map. 5.3.4 Spatial data warehouse The design of a data warehouse is quite different from designing relational database. While in the relational databases, the Entity Relationship (ER) 27 diagram is used, whereas in data warehouse projects, dimensional modelling is the most appropriate technique. The two main features of this modelling type is the possibility to use OLAP to query the data warehouse and the goal of a de-normalized schema instead of the normalized, which is preferred in ER modelling. 27 http://msdn.microsoft.com/en-us/library/bb955303.aspx 61 DESIGN AND IMPLEMENTATION Conceptual Model: The first step in data warehouse modelling is the conceptual design. Data is represented according to the data cube model. This model has four main concepts: Fact: It is a concept that is relevant for the decisional process. For example, in this project the facts are the solar radiation and the wind speed. Measure: A numerical property of the fact. For example, in this project the measures are the quantity of solar radiation and the quantity of wind speed. Dimension: A property of a fact described which respect to a finite domain. In the context of this project, a spatial dimension composed by a Latitude Dimension and a Longitude Dimension, and a temporal dimension, is necessary to characterize the facts. Hierarchy: The elements of a dimension can be organized as a hierarchy, a set of parent-child relationships, typically where a parent member summarizes its children. For example, the year member is the parent of the month member, and month is the parent of the day member. Figure 28 shows the conceptual modelling of the SDW used in Energy2People. Although only two dimensions are marked, in reality there are three dimensions. Latitude and Longitude can be seen as two different dimensions in the SpatialDimension. Figure 28: SDW conceptual model Logic Model: From the conceptual design, it is necessary to determine the logical schema of data. At this point, different models to represent multidimensional data can be used. These models are based on relational technologies (ROLAP), relational technologies with spatial DESIGN AND IMPLEMENTATION 62 support (SOLAP), multidimensional technologies (MOLAP) or a combination of both (Hybrid OLAP) 28 . In this approach, ROLAP and SOLAP model has been chosen. ROLAP and SOLAP use the relational data model, which means that data is stored in relations. The reason of using the two approaches is because SOLAP in final system is used just like a logical component, which means there is no native spatial support. In short, a ROLAP approach is used but it behaves like a SOLAP. Essentially, three schemas can be used the star schema, the snowflake schema and the constellation schema. The Star schema is used in this project. In computing, the star schema (also called star-join schema) is the simplest style of data mart schema. The star schema consists of one or more fact tables referencing any number of dimension tables. The star schema is an important special case of the snowflake schema, and is more effective for handling simpler queries. The star schema gets its name from the logical model's resemblance to a star with a fact table at its centre and the dimension tables surrounding it representing the star's points. Figure 29: SDW logic model Figure 29 shows the star schema used in the SDW. This model can be seen as a translation of the conceptual model into an ER model. And this is the result of using SOLAP/ROLAP approach. Both of them use a relational database in order to store data. One of the most important features of this model is that the table SpatialDimension in the logical model has latitude and longitude members while in conceptual model they can be seen as two different dimensions with their own hierarchies. Summarizing, this is the difference between the cube or dimensional model and the logic model. 5.3.5 Future work in SDW design As it is mentioned, during the design phase of the project it is tried to do some research in order to open new future work lines. One of the things that are tried to do is to combine complex geometries of administrative units with the pair latitude and longitude extracted from NASA website. Figure 30 shows a high level abstraction of the design goal. Data warehouse stores renewable energy quantity with their geometries and administrative names, for example, Spain -> Aragón 28 http://olap.com/w/index.php/Types_of_OLAP_Systems 63 DESIGN AND IMPLEMENTATION -> Zaragoza. However, the data marts contain only latitude/longitude pairs and renewable energy quantity. Figure 30: Abstraction of the future goal. Figure 31 shows the conceptual model of this approach. It can be infer from the figure below that SpatialDimension (the right one) has a hierarchy composed of four geometries. The smallest geometry is the point. In this case, the point is not a pair latitude/longitude but a geographical point. Likewise as a geometrical representation for province, region and country levels (adm_unit_level_3, adm_unit_level_2 and adm_unit_level_1). Figure 31: Future SDW logic model. The new SDW constructed need this conceptual modelling because in some decisions levels, knowing the name and the geometry of the administrative unit is really important, but when going deeper in the analysis, aggregations of huge amounts of points provides more meaningful insights on the data. DESIGN AND IMPLEMENTATION 64 Figure 32: Future SDW logic model The Figure 32 shows the logical model that is designed to be implemented in future. This model presents a snowflake schema. With this model, questions like, “How much solar radiation is available in Zaragoza for the last 20 years?” can be answered. What it allows is powerful new analytical features in the system. Aggregate tables design: When the system requirements demand high performance and low latency it is not just enough to design a good SDW. New strategies must be taken, and one of them is the use of aggregate tables. The aggregate table is essentially a pre-computed summary of the data in the fact table (Hyde, 2009). Although Energy2People version does not contain any aggregate tables, during the last phase of the project it is studied the impact of using this strategy in the system. For example, if the number of rows in the FactTable is about 2,737,500,000 (30 years × (365 o 366 days) × A matrix of 500×500 points), this can result in low performance and high latency. To resolve this problem, it is necessary to design some aggregate tables. The main problem of using this approach is that aggregate tables are an actually materialized view which means they are using physical space in the server. The Figure 33 shows an example of two aggregate tables that will be implemented in a future. 65 DESIGN AND IMPLEMENTATION Figure 33: Aggregate tables design Web Application Service layer: Once SDW and OLAP cubes are designed, it is necessary to access data via web. This can be done in two ways as stated below: 1. The Business logic retrieves data from a XMLA server. 2. The Business logic requests data from a REST API that abstracts data layer with the multidimensional model. The second option is considered feasible as it provides easy access to the data and reduces the latency effects. First of all, it is important to describe what a REST API service is. REST defines a set of architectural principles by which one can design the Web services that focus on a system resources, including how resource states are addressed and transferred over HTTP by a wide range of clients written in different languages. If measured by the number of Web services that use it, REST has emerged in the last few years as a predominant Web service design model. In fact, REST has huge impact on the web that it has mostly displaced SOAP and WSDL based interface design because it's a considerably simpler style to use (Rodriguez, 2008). So the reason behind using this approach is that queries that are available in the OLAP cube are pre-defined. So, giving some intuitive URLs, all queries can be mapped. In Energy2People system, service layer can be seen as mapping between requests via URLs and Multidimensional Expressions (MDX) queries. Every request triggers a MDX query that will retrieve data from data warehouse or data marts. The future goal of this REST service is to provide a simple API, where any developers could use to develop new models using renewable energy data and cubes. Business logic: As it is mentioned in the design section, a class diagram is used to design web application. It is also said that two main design patterns are used, the Facade pattern and the DESIGN AND IMPLEMENTATION 66 DAO pattern. This brings a powerful capacity of scaling new functionality in the future because these patterns are used to abstract details of implementation to other system classes. Figure 34 shows a more extensive class diagram than the presented in the main document. It is also a high level diagram because there are some levels of private methods that are considered to add noise to the schema. The fact is that as there is only one developer in this project the communication of designs have been always developed in high level without detailing some implementation details that are usually changed. Figure 34: Business logic class diagram. Two classes that do not appear in main document are the StyleCreator class and StyleChooser class. KMLRender class uses these classes in order to create some specific styles in the KML file, and the StyleChooser selects one of these styles for each one of the data. Finally, it must be said that in the last phase of the project some other functionalities are studied in order to improve system. One of these functionalities is the support of the GeoJSON format. Thanks to the design, the addition of this new class does not modify the behaviour and the structure of the rest of the components of the system. 5.4 Implementation This section describes the implementation part of the solution. The implementation is divided into three tasks such as ETL implementation, SOLAP and web application. 5.4.1 ETL The ETL process is implemented using Java programming language and Geokettle tool. Other frameworks such as Spring Batch 29 are analysed to address the implementation goals but they 29 http://docs.spring.io/spring-batch/ 67 DESIGN AND IMPLEMENTATION were not selected as their technical functionalities are limited and much less matured than the chosen tools. Geokettle 30 is a spatial ETL tool dedicated to the integration of different spatial data sources for building and updating geospatial data warehouses. In order to achieve this integration, Geokettle uses steps, transformation and jobs. Steps are simple processes, which achieve little transformations in data. Transformations are sequential steps that transform data from a source. A job is a group of transformations. This tool provides a framework where steps can be for example SQL scripts, JavaScript programs, and geospatial transformations. Extraction, transform and load steps are described below: Extraction One of the basic requirements of this project is to extract data from NASA web site. The problem is that this data was only accessible via browser. One click event is needed for every pair of latitude/longitude. Extracting the data manually is tedious and has no sense for extracting huge data, for this reason it is necessary to automate this task via web scraping techniques. Moreover, Geokettle does not include anything that could be used to do web scraping. So a web scraper is implemented using Java. This web scraper is a batch program that extracts data from NASA website in text file format and loads it in a table of a PostgreSQL database. Although Geokettle provides many pre-configured processes in order to implement ETL processes, specifically it does not implement anything that can be considered as a web scraper. Figure 35: Web Scraper configuration file. One of the main goals when designing and implementing scraper is flexibility and configurability. Different behaviours have been implemented to achieve scrapping. One thread, multiple threads, polite behaviour waiting a specific time before requests, etc. So the achieved goal is that user software administrator does not need to code any lines. Only “touching” configuration file, as the shown in Figure 35, extraction of data can be customized. 30 http://www.spatialytics.org/projects/geokettle/ DESIGN AND IMPLEMENTATION 68 Figure 36: How Scraper interacts with the NASA website. 75 DESIGN AND IMPLEMENTATION Figure 44: ETL flow implementation in Geokettle Transform Spatial Stage Table: Some transformations could be carried out, but they are beyond the scope of the project. For example, the name of the different administrative units could be translated in different languages in order to provide a multi language support. Load After transforming data, load data in data warehouse, according to the models presented in design sections, is the last step in the ETL. Next steps: Four tables with geospatial data of different administrative levels. A table with renewable energy measures, time and location where these measures take place. The load process has different steps. All of them are SQL scripts whose goal is to load all data with a snowflake schema of data warehouse and a star schema in the case of data marts. Spatial Dimension Loading To create table ‘Spatial Dimension Level 4’ with distinct pairs latitude/longitude and “Coordinate” column extracted from ‘NASA Stage Table’. To create two new columns in ‘Spatial Dimension Level 4’. First with a primary key, second one is going to point one of the tables obtained from shape files. To do a spatial join between ‘Spatial Dimension Level 4’ and ‘Spatial Dimension Level 3’. Fact Table Load: Create table “Fact Table”. Insert renewable energy measures and create foreign keys pointing to ‘Spatial Dimension Level 4’ and ‘’Temporal Dimension”. At these point data warehouse is created with a snowflake schema. DESIGN AND IMPLEMENTATION 76 Once the SDW is deployed, it is recommended to divide it in data marts. A data mart is the access layer of the data warehouse environment that is used to get data out to the users. The data mart is a subset of the data warehouse that is usually oriented to a specific business line or team. Data marts are small slices of the data warehouse. Whereas data warehouses have an enterprise-wide depth, the information in data marts pertains to a single department. LucidDB 34 is the DMBS that is used to implement data marts. LucidDB is a column-oriented DBMS, and aggregations are computed faster than relational DBMS. LucidDB is an open source RDBMS purpose–built entirely for data warehousing and BI. It is based on architectural cornerstones such as column-store, bitmap indexing and hash/join aggregation. In data marts, information is inside the last administrative level (local level), so geometries are not necessary and latitude/longitude pairs are enough to do an accurate analysis. Dividing data warehouse into some data marts, a faster access can be observed. The technical reason is because of the less number of rows in the table FactTable. This can be a really important point for future improvements and further studies. For example, cities like Zaragoza and Barcelona can have their own data mart with their own data. 5.4.2 SOLAP After the ETL process, a SDW is deployed and the next step is to build OLAP cubes. Mondrian 35 is considered the best and feasible option because of its good documentation support and the growing community. The problem with Mondrian is that is not a real spatial-enabled OLAP. But this project has demonstrated, with conceptual modelling, an OLAP cube implemented with Mondrian can be used as logic SOLAP. Also during the phase of the project, GeoMondrian 36 is used to implement as a more powerful SOLAP. However, the response latency is not considered good enough and a further detailed study on implementation can be considered for future work. In order to implement logical model, Mondrian uses schemas. A schema defines a multidimensional database. It contains a conceptual model, cubes, hierarchies, and members, and a mapping of this model onto a physical model. The conceptual model consists of the constructs used to write queries in MDX language: cubes, dimensions, hierarchies, levels, and members. The physical model is the source of the data that is presented through the logical model. It is typically a star schema, which is a set of tables in a relational database. The most important components of a schema are: A cube is a collection of dimensions and measures in a particular subject area. A measure is a quantity that user interested in measuring. 34 http://www.luciddb.org/html/main.html 35 http://mondrian.pentaho.com/ 36 http://www.spatialytics.org/projects/geomondrian/ 77 DESIGN AND IMPLEMENTATION A dimension is an attribute, or set of attributes, by which you can divide measures into sub-categories. A member is a point within a dimension determined by a particular set of attribute values. For example, 1997, 2012, 5 (May) and 31 are members of the Time Hierarchy. A hierarchy is a set of members organized into a structure for convenient analysis. A level is a collection of members that have the same distance from the root of the hierarchy. A dimension is a collection of hierarchies that discriminate on the same fact table attribute. Figure 45 presents the final schema in a graphical way. It can be seen that the main parts that are described in conceptual design phase are present in this figure. Figure 45: A graphic view of the implemented cube. But what Mondrian really uses in order to retrieve data and communicate with the physical data layer is a XML file. In this file, the schema with its measures, dimensions, hierarchies and their levels are defined. Figure 46 shows the final XML file that is executed in the project. It seems clear that XML file represents the multidimensional model that differentiates an OLAP cube between other alternative data structures. DESIGN AND IMPLEMENTATION 78 Figure 46: XML file of the implemented cube. 79 DESIGN AND IMPLEMENTATION SOLAP Future Work GeoMondrian 37 is the first implementation of a native SOLAP server. It provides a consistent integration of spatial objects into the OLAP data cube structure, instead of fetching them from a separate spatial database, web service or GIS file. GeoMondrian provides integration of spatial and non-spatial data types at a lower level. It directly interfaces with the Mondrian OLAP server and extends the capabilities of the OLAP server to handle spatial data. The OLAP server, in turn, uses a spatially enable database system, for example, PostGIS, as storage for data and to execute data queries. The OLAP server’s responsibility is to translate Spatial OLAP queries issued by the user into sequences of SQL queries that are being evaluated by the underlying database engine. The extensions that GeoMondrian provides only enable support for spatial data types but do not change the strategies used by Mondrian to evaluate OLAP queries. “This prevents these systems from efficiently answering queries on complex spatial dimension hierarchies, such as non-strict dimension hierarchies.”(Barltzer, 2011) Figure 47 shows how the SpatialDimension is defined when using this kind of native SOLAP. In this case, as it is described in design section, a snowflake schema is modelled, and in the schema this is represented with joins between tables. It is also important to point that in the levels, it is defined a property of the type Geometry. This allows Geomondrian to retrieve geometry columns from the SDW. Figure 47: XML file with the future spatial dimension. 37 http://www.spatialytics.org/projects/geomondrian/ DESIGN AND IMPLEMENTATION 80 Aggregate table’s implementation Unlike many OLAP servers, Mondrian does not store the data on disk: it just works on the data in the RDBMS, and when it reads a piece of data, it stores in its cache. This greatly simplifies the process of installing Mondrian, but it puts limits on Mondrian's performance when Mondrian is applied to a huge dataset (Hyde, 2009). Aggregate tables are a way to improve Mondrian's performance when the fact table contains a huge number of rows: a million or more. An aggregate table is essentially a pre-computed summary of the data in the fact table. An aggregate table coexists with the base fact table, and contains pre-aggregated measures built from the fact table. It is registered in Mondrian's schema, so that Mondrian can choose whether to use the aggregate table rather than the fact table, if it is applicable for a particular query. Designing aggregate tables is a fine art. There is extensive research, empirical and theoretical materials available on the web pertain to different ways to structure aggregate tables. A fact table can have zero or more aggregate tables. Every aggregate table is associated with just one fact table. It aggregates the fact table measures over one or more of the dimensions. The problem of aggregate tables is that they are materialized views. A materialized view is a database object that contains the results of a query, what it means that they are using space in the DBMS and space is usually an expensive resource in BI projects because of humongous information that is involved. Primarily, there are three important steps in order to implement aggregate tables: To create the aggregate table, to populate the aggregate table and to reference the aggregate table in Mondrian Schema. 5.4.3 Web application The service layer, the business logic layer and the presentation layer with their corresponding technologies are presented below. Service Layer Once SDW and SOLAP are implemented, the next step us to implement data access layer. As it is mentioned in previous sections, an REST API, which abstracts the multidimensional model, provides the connection between business logic layer and data layer. To implement this service, a framework called Jersey is selected. The reason for using this framework is that it extends and simplifies the JAX-RS API, the standard Java API to create RESTful Services. Moreover Jersey is open source and provides a lot of documentation support. Resources are the key parts that compose a RESTful Web service. Manipulating resources using HTTP methods like GET, POST, PUT, and DELETE. Anything in the application can be a resource, in this case renewable energy data. In JAX-RX, each resource is implemented using the pattern Plain Old Java Object using @Path annotation to compose their identifier. A resource can also have sub resources. 81 DESIGN AND IMPLEMENTATION There are some more annotations that help to implement this layer: • @Context: annotate to inject the contextual objects such as Request, Response, UriInfo, ServletContext, and so on. • @Path("{v1}"): This is the @Path annotation combined with the root path "/rest" that forms part of the URI. • @PathParam ("measure"): This annotation injects the parameters into the path, measure (wind speed and solar radiation), in this case. Other available annotations are @FormParam, @QueryParam, and so on. • @Produces: Multiple MIME types are supported for responses. In Energy2People case, application/json will be the default MIME type. At this point data warehouse, OLAP cube and the API REST are implemented. This implies that an important part of the backend is implemented. Now, it is necessary to implement a business logic layer where retrieved data can be processed and used to provide map visualization or to complete a financial or ecological model. These layers are implemented in Java programming language. Finally, a presentation layer, where users can interact with the application, is provided. This last layer is implemented using web technologies such as HTML, JavaScript and CSS. Business Logic Layer The business logic layer uses service layer to retrieve data required from the user when user interacts with presentation layer. Once the data is retrieved, different operations can be applied to enrich the data. As mentioned in Design section, logic is divided in different classes. They all are implemented in Java. To simplify the description of its implementation, they are divided in DAO, visualization and model. DAO: The Data Access Object (DAO) pattern is chosen to design access to service layer. More details of this chosen are described in Design section. The selection of JSON format to return data from service layer is considered to be a good choice because of its lightness and the support in Java language. Summarizing the functionality of this part of the system, the implementation of RenewableEnergyDaoImpl converts the user request via in an APIRESTcall. To implement connection with the API REST, Jersey Library is used. Once data is retrieved in JSON format, this is mapped into a JSONArray object from the JSON library for Java. At this point, the data is sent to the Facade class, which is also implemented in Java. This class can now send the data to the KmlRenderor to the EcoFinancialModel class. Visualization: If user is interacting with the map in the presentation layer, when data is retrieved from data layer, it is converted to KML. To implement this functionality two java libraries are essential: Java API for KML (JAK) and JSON library for Java. The JSON library for Java is used to turn the input stream from the service layer into java objects, in this case JSONObject and JSONArray. DESIGN AND IMPLEMENTATION 82 The main goal of the Java API for KML (JAK) is to provide automatically generated full reference implementation of the KML object model defined by OGC's KML standard and Google's GX extensions. It is an object orientated API that enables the convenient and easy use of KML in existing Java environments. On one hand, KML provides a powerful way to display georeferenced data on Google Earth. It is an XML notation for expressing geographic annotation and visualization within Internetbased, two-dimensional maps and three-dimensional earth browsers. KML was developed for use with Google Earth. KML became an international standard of the Open Geospatial Consortium (OGC) in 2008. It specifies a set of features (place marks, images, polygons, 3D models, textual descriptions, etc.) for display in Google Earth, Google Maps and Mobile, or any other geospatial software implementing the KML encoding. Each place always has a longitude and latitude. Other data can make the view more specific, such as tilt, heading, altitude, which together define a "camera view" along with a timestamp or time span. KML shares some of the same structural grammar as GML. On the other hand, one of the problems of using KML as output format is the size of resulting file. This results in high latency, but Apache Software Foundation provides a library called Commons IO, which speeds up the file serialization. Then, it is send to presentation layer. During the last phase of the project, some other kinds of formats were also tired, in particular, GeoJSON. GeoJSON is a format for encoding a variety of geographic data structures. A GeoJSON object may represent geometry, a feature, or a collection of features. GeoJSON supports the following geometry types: Point, LineString, Polygon, MultiPoint, MultiLineString, MultiPolygon, and GeometryCollection. Features in GeoJSON contain a geometry object and additional properties, and a feature collection that represents a list of features. Model: The model presented in the next lines is developed is based on the reading of several sources, and simplicity in the implementation. The system requirements do not specify complex models, just the possibility to add models to the retrieved data. With these models, the Energ2People project aims to provide a simple and fast translation between data units. In the future versions of this project, quality of these models must be improved in order to have better findings and accurate results. Presentation Layer In presentation layer user can interact with the application for functional and usability requirements. The technologies that are used and their mission are explained below: Hypertext Markup Language (HTML), the mark-up language used to create documents on the World Wide Web. HTML defines the structure and layout of a Web document by using a variety of tags and attributes. JavaScript, jQuery and AJAX: JavaScript is an interpreted computer programming language implemented as part of web browsers so that client-side scripts could interact with the user, control the browser, communicate asynchronously, and alter the document content that was displayed. Ajax is a group of interrelated web development techniques used on the client-side 83 DESIGN AND IMPLEMENTATION to create asynchronous web applications. JQuery 38 is a multi-browser JavaScript library designed to make it easier to navigate a document, select Document Object Model (DOM) elements, to handle events and some other features. Cascading Style Sheets (CSS) and Twitter bootstrap: CSS is a style sheet language used for describing the presentation look and formatting of a document written in a markup language. In order to standardize development of interface components, Twitter bootstrap 39 has been used beside jQuery-UI. Twitter Bootstrap is a free collection of tools for creating websites and web applications. Google Maps: The Google Maps JavaScript API lets Google Maps geo content you embed in any of the web pages. This JavaScript library has great importance in this project for spatial dissemination of data on the web. This web application uses Google geo component to send Ajax requests to business logic layer. When zooming or panning on the map, new data is retrieved. When this occurs, the business logic transforms the data in the KML files that can be displayed on the map. Otherwise, if the user uses menu to apply a model to the data, the table displays the results. These results are sent to the presentation layer in JSONformat. Thus, all these technologies played significant and important role in design and implementation of the Energy2People application. 5.5 Qualitative assessment Although a quantitative study on the performance of the tools would provide better insights for assessing on improvement areas, due to several constrains such as limited control over the technical details of other software architectures presented in this section and also due to stipulated time, the assessment of the tool has been made qualitatively. Following the guidelines presented in Spencer et al. (2003), some arbitrary indicators pertained to design, analysis and reporting are presented which will help in assessing features of the tool presented in this chapter. This comparison assessment allows the tool approach to cross-reference with other existing tools and to add pragmatic support to the theory proposed in the thesis. Since, this is a brief assessment of the tool; eight attributes that are considered as the most relevant from thesis point of view, general software engineering and user perspective. Assessment attributes are Data granularity, Visualization, Reports, Data coverage, Services, Data sources, Latency and Ease of use. Like mentioned in earlier chapter, we choose two global player tools from NREL 40 and IRENA 41 and, a region tool called Energie-Atlas Bayern 42 . A brief comparison to these tools with our tool (Energy2people) is laid below and as shown in the Table 5. 38 http://jquery.com/ 39 http://getbootstrap.com/ 40 http://maps.nrel.gov/re_atlas 41 http://www.irena.org/globalatlas/ 42 http://geoportal.bayern.de/energieatlas-karten DESIGN AND IMPLEMENTATION 84 Functionality NREL IRENA Energie-Atlas Bayern Energy2people Data granularity Single level Single level Single level Multiple level Visualization Initiativesingle level Initiativesingle level Initiativesingle level Initiativemultiple level Dashboards/ Reports Spatial Spatial Spatial, Chats Spatial, Chats and Tables Data coverage USA Global Bavaria, Germany SpatialSpain DatabaseGlobal Renewable Energy services Hydro, Geothermal, Biomass, Solar and Wind Solar and Wind Biomass, Geothermal, Solar, Hydro and Wind Solar, Wind and Solar ∩ Wind Data sources ESRI Various In house NASA Table 5: Qualitative assessment of Renewable Energy explorers Data granularity: Majority of the decision-making application is largely dependent on representativeness of various use case scenarios. For example, retailers like Mc Donald’s business need data patterns for sales and expansion of business. One can analyse or visualize for various data granularities. For such business operations, one needs data at various levels. For example, what is today sale at X location verses Y location? What are the sales three months ago at same location? What is monthly, yearly revenue at X location? Likewise, in RE field data granularities play vital role to evaluate selective scenarios as well as complete profile using random selection. The Data granularity functionality of Energy2people as shown in Figure 48 and Figure 49 represents data at multiple levels based on the user’s choice where as other tools are limited to abstract level at single level. They do not offer search or information at local or regional level. There is no provision for the user to select the data monthly, yearly, geographical at different levels. The scientific value of this tool is representativeness of meaningful data and interesting insights at different levels down both on spatial and nonspatial formats. The layer management style creates bi-directional communication to immediate next higher-level layer or lower-level layer based on the user selection. 91 CONCLUSION studies. REDSS profound objective is to propose a web service that will accommodate various spatiotemporal ground truth when and where scenarios, in which an expert user to common public can perform from the ground up sunshadow data analyses. Chapter 5 (C5) unfolded Energy2People a web application which has been evaluated and recognised by NASA scientists and experts. This is a major pragmatic contribution from this thesis to be recognised by NASA as a one of the best matter-of-fact approach for “Renewable Energy Explorer”. The tool fits in Open business model as this economical built on open source components and free available data source. As mentioned earlier this tool as a “World class” on par with or above international standard web services. The distinguishable quality of tool is, “it discovers and renders inherent data relationships and, establishes a meaningful relationship among the data entities in cases where inheritance is absent” (Figure 48). The thesis ends with the presentation of Energy2people prototype that is based on BI approach. This thesis takes pride in working with diverse and complex topics nevertheless could able to come up with a solution (REDSS/Energy2people). As the selected thesis topic is vast and complex it leaves for lot of scope for avenues of research. As mentioned earlier in the thesis, the future work list mentioned below has potential to be a thesis topic for Computer sciences, Geoinformatics, Renewable energy science students. 6.2 Future work Sun-shadow mapping is very vast and complex subject. It involves various concepts from different subjects like Astronomy, Physics, Environment, Computer science and Location based information technology. Integrating concepts from various fields to build a complete sun shadow-mapping environment opens up great possibilities for taking this further. Due to limited availability of resources, time constraints and other factors, REDSS proposed cannot be developed fully-fledged and this leaves to have substantial scope for future work. Below are some important highlights that need immediate attention in making use of complete clean energy sources. In addition, few technical future work points are also highlighted in the Design and Implementation section of Chapter 5. All these are very demanding but at the same should be very interesting for various real world use cases such as PV plants (installation cost, site selection, estimation of useful rooftop area), sun and shadow paths etc. 6.2.1 Eclectic approaches As in relation to future work, it will be interesting to delve on eclectic approaches for integrating diverse models for developing the digital earth model and also on the automation of QC on the data to explore the possibility of improving the quality of data for analytical and sophisticated applications. 6.2.2 Wind component As discussed in this thesis, mapping solar energy has lot of benefits especially for PV plants. Discovering best location for both wind energy and solar radiation should be interesting finding for the project. As mentioned earlier in this thesis, wind energy and solar radiation act CONCLUSION 92 as complimentary sources. As per theory, to set-up these renewable energy sources does not require much water which is also a plus point for choosing these clean energy resources. Considering all these points in finding optimal suitable location will be challenging but the results should add great economical value to the existing system. 6.2.3 Dimensions The Sun and Shadow cubes proposed in this thesis have several dimensions and has its own significance to each dimension and hierarchical levels. These cubes can be expanded more in terms of intuitiveness, functionality, analysing, economics etc. For example, it could be interesting to the PV plant decision makers to have a dimension based on type of PV. Standard Crystalline Silicon (c-Si), Cadmium-telluride, Copper-indium-gallium-selenide (CIGS) are some of which categorized with conversion efficiency ranging from 20 % down to 7 % (Agugiaro et al. 2012). 6.2.4 Solar intensity It will be very interesting to have one single equation for solar intensity, which considers all atmospheric parameters such as Temperature, Dew point, WindSpeed, Humidity, Skycover, and Precipitation. As used in Energy2People, NASA provides data to all these parameters. It would be really interesting for PV applications, if a relation to all these atmospheric parameters to the solar radiation data were established. Further, improvement on the data quality and data coverage of these parameters will be more useful for PV applications. 6.2.5 Checkerboard graphs Cubes for solar radiation will play significant role in site selection which is explained in this thesis. Adding, Checkerboard graphs will be very interesting graph plot for each atmospheric parameter (Temperature, Dew point, WindSpeed, Humidity, Skycover, and Precipitation) along with solar radiation data as faces of the cube. Figure 51 shows the checkerboard for solar radiation data of a particular region along the year and month. Adopting such plotting to data cube for temporal hierarchical (Month---Week---day) structuring on each face the Data cube could yield interesting patterns for more intuitive decision-making. 93 CONCLUSION Figure 52: Checkerboard graphs for Global Horizontal Irradiance (Kaku and Potter 2009). 6.2.6 Interoperability As this thesis encompass on Open business models, open data and SDIs. Interoperability should be one of the interesting areas for the computer science experts to research on the interoperability capabilities for software development and the Geo BI solutions. 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