Dynamic Daylight Metrics for Electricity Savings in Offices: Window Size and Climate Smart Lighting Management

energies Article Dynamic Daylight Metrics for Electricity Savings in Offices: Window Size and Climate Smart Lighting Management Ignacio Acosta 1, Miguel Ángel Campano 1, Samuel Domínguez-Amarillo 1,* and Carmen Muñoz 2 1 Instituto Universitario de Arquitectura y Ciencias de la Construcción, Universidad de Sevilla, 41004 Sevilla, Spain; [email protected] (I.A.); [email protected] (M.Á.C.) 2Departamento de Construcciones Arquitectónicas, Universidad de Málaga, 29016 Málaga, Spain; [email protected] *Correspondence: [email protected]; Tel.: +34-954-556-595 Received: 20 July 2018; Accepted: 10 November 2018; Published: 13 November 2018   Abstract: Daylight performance metrics provide a promising approach for the design and optimization of lighting strategies in buildings and their management. Smart controls for electric lighting can reduce power consumption and promote visual comfort using different control strategies, based on affordable technologies and low building impact. The aim of this research is to assess the energy efficiency of these smart controls by means of dynamic daylight performance metrics, to determine suitable solutions based on the geometry of the architecture and the weather conditions. The analysis considers different room dimensions, with variable window size and two mean surface reflectance values. DaySim 3.1 lighting software provides the simulations for the study, determining the necessary quantification of dynamic metrics to evaluate the usefulness of the proposed smart controls and their impact on energy efficiency. The validation of dynamic metrics is carried out by monitoring a mesh of illuminance-meters in test cells throughout one year. The results showed that, for most rooms more than 3.00 m deep, smart controls achieve worthwhile energy savings and a low payback period, regardless of weather conditions and for worst-case situations. It is also concluded that dimming systems provide a higher net present value and allow the use of smaller window size than other control solutions. Keywords: daylight autonomy; window design; energy saving; smart control 1. Introduction and Objectives 1.1. State of the Art Energy saving is one of the key variables in present-day building construction and civil engineering. In fact, lighting represents between 15% and 30% of power consumption in buildings [ 1 – 3 ]. Accordingly, suitable use of daylight is essential in reducing the power consumption of electric lighting [ 4 ], while the development of new technologies, such as the improvement of LED lamps or lighting smart controls, can help promote a lower impact on the environment [5]. Lighting smart controls were introduced in the early 2000s to promote energy saving in buildings. One of the first outstanding examples of their use in buildings is the New York Times Headquarters, where a lighting dimming system based on occupancy and daylight availability achieved an energy saving of close to 40% in the floor perimeters [ 6 ]. Subsequently, lighting smart controls were used in other buildings of note [7,8] with a noticeable energy saving in lighting. Energies 2018,11, 3143; doi:10.3390/en11113143 www.mdpi.com/journal/energies Energies 2018,11, 3143 2 of 27 Smart control strategies require the assessment of different variables: the window size is essential to determine electricity consumption [ 9 ], as are the reflectance of the room surfaces and the location of the building [ 10 ]. The illuminance controls also have a significant impact on the thermal comfort of occupants, mainly due to solar heat gain, so that algorithms should take all possible variables into account [ 11 ]. One of the most common smart controls is that of dimming systems [ 12 , 13 ], which adjust the electricity power of the luminaires. The dimmer can be controlled by illuminance-meters which significantly reduce the power consumption in electric lighting [14,15]. According to the latest studies [ 16 – 18 ], the strategies for lighting control can reduce power consumption by up to 50% when using dimming systems and close to 30% using occupant detectors. However, these technologies are not widespread, given the difficulties in installation, the limitations of the prediction algorithms, and the individual management preferences of occupants [ 19 – 21 ]. It is therefore important to emphasize the benefits from all these strategies, quantifying their energy efficiency and economic profitability, and avoiding an overestimation of the savings or overly optimistic analysis. Hence, the use of dynamic daylight metrics will provide a better whole-year fit. Unlike daylight static concepts, dynamic metrics allow the accurate quantification of energy savings in electric lighting, including variables not considered by static metrics, such as weather conditions, occupancy hours or illuminance thresholds required by the task being carried out [22–24]. 1.2. Aim and Objectives This research aims to assess the energy efficiency and economic profitability of lighting smart controls, determining suitable solutions according to the geometry of the architecture and the weather conditions, mainly based on the use of affordable technologies and with the lowest possible implementation cost, and with a special focus on retrofitting office buildings. The analysis of the energy efficiency of the smart controls is deduced from the assessment of dynamic daylight performance metrics using two lighting simulation programs. The first program is Dialux 4.12, used to determine the power consumption of electric lighting to achieve an illuminance threshold, while the second, DaySim 3.1, establishes when the illuminance threshold is met by daylight alone. According to the results provided by both programs, the turn-on time of the electric lighting can be programmed, and the power consumption can be evaluated under different control proposals. The economic profitability study is based on the analysis of the net present value, detailed in the initial investment costs of the smart controls, and the predicted minimal assured savings in the annual electricity bill—thus the use of the dynamic annual metric. This study examines the suitability of each smart control according to the room dimensions and location. The novelty of this study is based on the analysis of daylight dynamic metrics including continuous daylight autonomy, instead of the classical approach based on static metrics—i.e., Daylight Factor—combined with the impact of the proposed solutions on economic profitability. Energy consumption and implementation of these solutions are studied in offices. Dynamic daylight performance metrics have never been used before for quantification of the effect of dimming smart controls. This research also includes a validation of the abovementioned metrics by means of the monitoring of illuminance-meters in test cells under real conditions. 2. Description of Methodology for Calculation 2.1. Characteristics of the Room Model 2.1.1. Geometry of the Room Model As an example of the most common office room, a virtual venue 3.00 m high with variable depth and width was used to calculate the dynamic daylight metrics and the energy saving in electric lighting. The room represents an open-plan prototypical office, where it is necessary to maintain an office-task light field suitable for the entire area. The thickness of the room walls, ceiling and floor is 0.25 m, Energies 2018,11, 3143 3 of 27 considering a variable reflectance of the inner surfaces as well as a diffuse reflection. Thus, the light reflected is directly proportional to the cosine of the angle between the observer’s line of sight and the surface normal. The different sizes of openings in the façade are defined as surface ratios. The window opening is double glazed with a visible light transmittance of 0.70 and 0.05 m thick joinery. The virtual room and calculation variables are shown in Figure 1and Table 1. The study points at which dynamic daylight performance metrics were analyzed were positioned on equidistant axes at a height of 0.70 m with a spacing 0.75 m wide and 0.25 m deep, as shown in Figure 1. Energies 2018, 11, x FOR PEER REVIEW 3 of 27 the light reflected is directly proportional to the cosine of the angle between the observer’s line of sight and the surface normal. The different sizes of openings in the façade are defined as surface ratios. The window opening is double glazed with a visible light transmittance of 0.70 and 0.05 m thick joinery. The virtual room and calculation variables are shown in Figure 1 and Table 1. The study points at which dynamic daylight performance metrics were analyzed were positioned on equidistant axes at a height of 0.70 m with a spacing 0.75 m wide and 0.25 m deep, as shown in Figure 1. Figure 1. Calculation model. Table 1 shows 18 room models established according to a variable depth (values of 3, 6 and 9 m), window size (window-to-façade ratio from 30 to 90%) and the reflectance of the inner surfaces. Table 1. Room models according to variables defined. Model Depth Window to Façade Visible Light Transmittance Joinery Reflectance Ceiling Reflectance Wall Reflectance Floor Reflectance Locations 330B 3 m 30% 0.7 0.9 0.8 0.6 0.4 Stockholm (S) London (L) Madrid (M) 360B 3 m 60% 0.7 0.9 0.8 0.6 0.4 Stockholm (S) London (L) Madrid (M) 390B 3 m 90% 0.7 0.9 0.8 0.6 0.4 Stockholm (S) London (L) Madrid (M) 330D 3 m 30% 0.7 0.9 0.6 0.4 0.2 Stockholm (S) London (L) Madrid (M) 360D 3 m 60% 0.7 0.9 0.6 0.4 0.2 Stockholm (S) London (L) Madrid (M) 390D 3 m 90% 0.7 0.9 0.6 0.4 0.2 Stockholm (S) London (L) Madrid (M) 630B 6 m 30% 0.7 0.9 0.8 0.6 0.4 Stockholm (S) London (L) Madrid (M) 660B 6 m 60% 0.7 0.9 0.8 0.6 0.4 Stockholm (S) London (L) Madrid (M) 690B 6 m 90% 0.7 0.9 0.8 0.6 0.4 Stockholm (S) London (L) Madrid (M) 630D 6 m 30% 0.7 0.9 0.6 0.4 0.2 Stockholm (S) London (L) Madrid (M) 660D 6 m 60% 0.7 0.9 0.6 0.4 0.2 Stockholm (S) London (L) Madrid (M) Figure 1. Calculation model. Table 1shows 18 room models established according to a variable depth (values of 3, 6 and 9 m), window size (window-to-façade ratio from 30 to 90%) and the reflectance of the inner surfaces. Table 1. Room models according to variables defined. Model Depth Window to Façade Visible Light Trans-mittance Joinery Reflectance Ceiling Reflectance Wall Reflectance Floor Reflectance Locations 330B 3 m 30% 0.7 0.9 0.8 0.6 0.4 Stockholm (S) London (L) Madrid (M) 360B 3 m 60% 0.7 0.9 0.8 0.6 0.4 Stockholm (S) London (L) Madrid (M) 390B 3 m 90% 0.7 0.9 0.8 0.6 0.4 Stockholm (S) London (L) Madrid (M) 330D 3 m 30% 0.7 0.9 0.6 0.4 0.2 Stockholm (S) London (L) Madrid (M) 360D 3 m 60% 0.7 0.9 0.6 0.4 0.2 Stockholm (S) London (L) Madrid (M) 390D 3 m 90% 0.7 0.9 0.6 0.4 0.2 Stockholm (S) London (L) Madrid (M) 630B 6 m 30% 0.7 0.9 0.8 0.6 0.4 Stockholm (S) London (L) Madrid (M) 660B 6 m 60% 0.7 0.9 0.8 0.6 0.4 Stockholm (S) London (L) Madrid (M) 690B 6 m 90% 0.7 0.9 0.8 0.6 0.4 Stockholm (S) London (L) Madrid (M) 630D 6 m 30% 0.7 0.9 0.6 0.4 0.2 Stockholm (S) London (L) Madrid (M) 660D 6 m 60% 0.7 0.9 0.6 0.4 0.2 Stockholm (S) London (L) Madrid (M) 690D 6 m 90% 0.7 0.9 0.6 0.4 0.2 Stockholm (S) London (L) Madrid (M) 930B 9 m 30% 0.7 0.9 0.8 0.6 0.4 Stockholm (S) London (L) Madrid (M) 960B 9 m 60% 0.7 0.9 0.8 0.6 0.4 Stockholm (S) London (L) Madrid (M) 990B 9 m 90% 0.7 0.9 0.8 0.6 0.4 Stockholm (S) London (L) Madrid (M) 930D 9 m 30% 0.7 0.9 0.6 0.4 0.2 Stockholm (S) London (L) Madrid (M) 960D 9 m 60% 0.7 0.9 0.6 0.4 0.2 Stockholm (S) London (L) Madrid (M) 990D 9 m 90% 0.7 0.9 0.6 0.4 0.2 Stockholm (S) London (L) Madrid (M) Energies 2018,11, 3143 4 of 27 A variable width (values of 4, 8 and 12 m) was considered to assess the energy savings according to the room surface, producing a total of 54 room models. This variable barely affects the calculation results of the dynamic metrics and is therefore only used to determine the cost-effectiveness metrics used to validate the lighting smart control from an economic perspective. 2.1.2. Location of the Room Model The room models defined were studied for three different locations—Stockholm, London and Madrid—representing a wide range of weather conditions and latitudes from 40 to 60 degrees, thus contributing to the analysis of the impact of latitude and sky luminance. Accordingly, the results obtained for Madrid could be extrapolated to the Mediterranean climate, while the conclusions for London and Stockholm could be assumed for other parts of Northern Europe. •Stockholm (Sweden): 60◦north latitude, mainly overcast skies. •London (UK): 50◦north latitude, predominantly overcast skies. •Madrid (Spain): 40◦north latitude, mainly clear skies. The weather data for these three locations were obtained from EnergyPlus Engineering Reference [ 25 ], using direct normal and diffuse horizontal irradiances, as well as from the sky model developed by Perez et al. [ 26 ] and accepted by CIE [ 27 ]. The files selected for Stockholm and London, STOCKHOLM-ARLANDA IWEC and LONDON-GATWICK IWEC (International Weather for Energy Calculations), were created and provided by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) [ 28 ]. The file selected for Madrid, MADRID SWEC (Spanish Weather for Energy Calculations), was created using data from Pérez-Lombard at the Spanish National Institute of Meteorology (AEMET) [29]. 2.1.3. Orientation of the Window All the windows in this study face north, avoiding direct sunlight since this is the worst-case scenario for indoor daylight illuminance values [ 30 ]. In office buildings facades facing north do not usually have blinds [ 31 , 32 ], since fundamentally their use can be limited to the control of the glare in the initial and final hours of the day, in many cases outside the working hours. The influence over the control of the annual gain of natural lighting is usually of little relevance [ 33 ]. This also allows a better comparison between locations. Despite the fact that the height of the lintel affects daylight penetration, it is not considered for this study, nor are the neighboring solar obstructions. The ground reflectance value used is 0.2, which is the default value recommended by DaySim and provided by the Radiance engine. 2.2. Lighting Design of the Room Model The electric lighting of the virtual room consists of the use of Erco Skim downlight oval LED floodlight luminaires (Figure 2). These 37 W luminaires, with a luminous flux of 3690 lm, are arranged in parallel lines following the depth of the room: a row of luminaires is placed every 3 m deep (one, two or three rows, depending on the depth of each model), beginning at 1.5 m from the window. This study considers an illuminance threshold of 500 lx, standard value for offices according to EN 12461-1:2012 [ 34 ]. Given that variations in the threshold cited would affect the results on the impact of energy efficiency from lighting smart controls, the illuminance value must be chosen carefully, based on use. The spacing between luminaires was calculated using Dialux 4.12, a simulation program validated by previous studies [ 35 ] and widely used in electric lighting design. In accordance with the results obtained, the separation between luminaires in each of the rows was optimized to achieve a uniformity value above 0.6 and an average illuminance of 500 lx, obtaining a spacing of 1.25 m, as can be seen in Figure 2. To obtain a value close to the illuminance threshold, the luminaires are initially dimmed to reach 500 lx on the work plane, thus adjusting electricity consumption. The luminous flux adjustment of Energies 2018,11, 3143 5 of 27 the lamps corresponds to 75% in those rooms with high reflectance values and to 90% for dark rooms. Defining the minimum luminous flux for each study case, the minimum energy savings promoted by lighting smart controls can be quantified. For the sake of comparison and to keep the model as simple as possible, the base case (Case study 0) will be wired to only one command circuit. Although in this type of room it is usual to have at last two (or three) switches, it is not uncommon to find the simple all-on/off situation—mainly in older buildings. Three alternative case studies were developed (Figure 2) based on the typical control approaches for daylight saving [36]: • Case study 1: Manual On/Off lighting control with two separate control rows: circuit 1 is for the near-façade lighting row and a second command control is for the remaining lighting rows. This system is only available for rooms 6 and 9 m deep. • Case study 2: Common Dimming lighting control for all the luminaires of the room (single controller). The dimmer is controlled by a lux-meter which detects daylight illuminance, adjusting the power supply for the lamps. • Case study 3: Two independent dimming-lighting-controls with two separate groups, where one circuit commands the near-façade lighting line and the other the remaining lighting rows. Systems are controlled by lux-meters which detect daylight illuminance, adjusting the power supply for the lamps. This system is only available for rooms 6 and 9 m deep. It is worth noting that the proposed luminaires only determine the power consumption in electric lighting, following optimal location and photometric distribution. According to the results of this brief study, the electric cost, and in turn the suitability of the proposed smart controls, can be defined by their initial investment costs. To determine the minimum energy saving of the dimming smart controls, it is assumed that occupants will not delay the activation of the switching controls to achieve the threshold of 500 lx (acting as perfect users [ 37 ]). Accordingly, the automatic response of the occupants serves to determine the tightest baseline scenario for quantifying the minimum energy savings achieved by the lighting smart controls. It is foreseeable that the savings will be higher the farther away these users are from the perfect user. It should be noted that this approach is based on daylight availability and on an ideal functioning of the control system, the response of which is actually affected by the calibration setting, the photo-sensors’ characteristics and the actual daylight fluctuations, as demonstrated in previous research [ 38 , 39 ]. Therefore, further research is necessary in order to evaluate energy savings in a more realistic operative mode. Moreover, as the location of the illuminance-meters affects the dimming control, these are located in the central axis of the room, 3 m from the façade (case 3) and at the back of the room near the inner wall to assure the threshold in all circumstances (worst case scenario). Energies 2018,11, 3143 6 of 27 Energies 2018, 11, x FOR PEER REVIEW 6 of 27 Figure 2. Lighting design of the room model and proposed smart controls. The power consumption of other LED lamps would be very similar to those chosen for this study, obtaining a value of energy efficiency in lighting close to 1.5 W/m2/100 lx for bright rooms and 1.7 W/m2/100 lx for dark rooms. In the case of halogen or fluorescent lamps, the energy efficiency would be noticeably poorer, consuming more energy than the luminaires selected. Therefore, LED lamps represent the most conservative scenario for calculating the suitability of smart controls. Since this study evaluates the initial investment costs of the proposed smart controls, Table 2 shows the economic costs of case studies 1, 2 and 3, including electric components, dimming control system, wiring and assembly costs. The total costs listed exclude the costs of the components of Case study 0, since these elements are common to all the case studies and can be considered a reference. Table 2. Estimated initial investment costs of the proposed smart controls. CASE 1: ON/OFF LIGHTING WITH SEPARATE ROWS FAÇADE-BACK MATERIAL Cost per unit Units Total cost Stockholm London Madrid Stockho lm London Madrid Electric cable, single-pole, section 1.5 mm2, crosslinked polyethylene insulation. 0.46 €/m 0.46 €/m 0.46 €/m 3.20 m 1.47 € 1.47 € 1.47 € Single-pole switch, medium range, rated voltage 250 V, according to EN 60669. 13.90 €/U 13.90 €/U 13.90 €/U 1.00 U 13.90 € 13.90 € 13.90 € Labor costs of electrical technician. 39.00 €/h 26.40 €/h 20.60 €/h 1.50 h 58.50 € 39.60 € 30.90 € INITIAL INVESTMENT COSTS 73.87 € 54.97 € 46.27 € CASE 2: DIMMING ALL LIGHTING MATERIAL Cost per unit Units Total cost Stockholm London Madrid Stockho lm London Madrid Arduino Uno Rev 3 chip Atmega328 for control system, including timer, servo and connections for pulse-width modulation (PWM) control. 35.00 €/U 35.00 €/U 35.00 €/U 1.00 U 35.00 € 35.00 € 35.00 € Electric transformer from 220/120 V to 10 V, including connections. 8.00 €/U 8.00 €/U 8.00 €/U 1.00 U 8.00 € 8.00 € 8.00 € Mounting box of high-density polyethylene, including connections. 12.00 €/U 12.00 €/U 12.00 €/U 1.00 U 12.00 € 12.00 € 12.00 € Figure 2. Lighting design of the room model and proposed smart controls. The power consumption of other LED lamps would be very similar to those chosen for this study, obtaining a value of energy efficiency in lighting close to 1.5 W/m 2 /100 lx for bright rooms and 1.7 W/m 2 /100 lx for dark rooms. In the case of halogen or fluorescent lamps, the energy efficiency would be noticeably poorer, consuming more energy than the luminaires selected. Therefore, LED lamps represent the most conservative scenario for calculating the suitability of smart controls. Since this study evaluates the initial investment costs of the proposed smart controls, Table 2 shows the economic costs of case studies 1, 2 and 3, including electric components, dimming control system, wiring and assembly costs. The total costs listed exclude the costs of the components of Case study 0, since these elements are common to all the case studies and can be considered a reference. Table 2. Estimated initial investment costs of the proposed smart controls. CASE 1: ON/OFF LIGHTING WITH SEPARATE ROWS FAÇADE-BACK MATERIAL Cost per unit Units Total cost Stockholm London Madrid Stockholm London Madrid Electric cable, single-pole, section 1.5 mm2, crosslinked polyethylene insulation. 0.46 €/m 0.46 €/m 0.46 €/m 3.20 m 1.47 €1.47 €1.47 € Single-pole switch, medium range, rated voltage 250 V, according to EN 60669. 13.90 €/U 13.90 €/U 13.90 €/U 1.00 U 13.90 €13.90 €13.90 € Labor costs of electrical technician. 39.00 €/h 26.40 €/h 20.60 €/h 1.50 h 58.50 €39.60 €30.90 € INITIAL INVESTMENT COSTS 73.87 €54.97 €46.27 € Energies 2018,11, 3143 7 of 27 Table 2. Cont. CASE 2: DIMMING ALL LIGHTING MATERIAL Cost per unit Units Total cost Stockholm London Madrid Stockholm London Madrid Arduino Uno Rev 3 chip Atmega328 for control system, including timer, servo and connections for pulse-width modulation (PWM) control. 35.00 €/U 35.00 €/U 35.00 €/U 1.00 U 35.00 €35.00 €35.00 € Electric transformer from 220/120 V to 10 V, including connections. 8.00 €/U 8.00 €/U 8.00 €/U 1.00 U 8.00 €8.00 €8.00 € Mounting box of high-density polyethylene, including connections. 12.00 €/U 12.00 €/U 12.00 €/U 1.00 U 12.00 €12.00 €12.00 € Lux-meter Adafruit TSL2561 Digital, spectral response according to standard photopic vision. 16.00 €/U 16.00 €/U 16.00 €/U 1.00 U 16.00 €16.00 €16.00 € Labor costs of electrical technician. 39.00 €/h 26.40 €/h 20.60 €/h 2.50 h 97.50 €66.00 €51.50 € INITIAL INVESTMENT COSTS 168.50 €137.00 €122.50 € CASE 3: DIMMING LIGHTING WITH SEPARATE ROWS FAÇADE-BACK MATERIAL Cost per unit Units Total cost Stockholm London Madrid Stockholm London Madrid Electric cable, single-pole, section 1.5 mm2, crosslinked polyethylene insulation. 0.46 €/m 0.46 €/m 0.46 €/m 3.20 m 1.47 €1.47 €1.47 € Single-pole switch, medium range, rated voltage 250 V, according to EN 60669. 13.90 €/U 13.90 €/U 13.90 €/U 2.00 U 27.80 €27.80 €27.80 € Arduino Uno Rev 3 chip Atmega328 for control system, including timer, servo and connections for PWM control. 35.00 €/U 35.00 €/U 35.00 €/U 2.00 U 70.00 €70.00 €70.00 € Electric transformer from 220/120 V to 10 V, including connections. 8.00 €/U 8.00 €/U 8.00 €/U 2.00 U 16.00 €16.00 €16.00 € Mounting box of high-density polyethylene, including connections. 12.00 €/U 12.00 €/U 12.00 €/U 2.00 U 24.00 €24.00 €24.00 € Lux-meter Adafruit TSL2561 Digital, spectral response according to standard photopic vision. 16.00 €/U 16.00 €/U 16.00 €/U 2.00 U 32.00 €32.00 €32.00 € Labor costs of electrical technician. 39.00 €/h 26.40 €/h 20.60 €/h 3.50 h 136.50 €92.40 €72.10 € INITIAL INVESTMENT COSTS 307.77 €263.67 €243.37 € 2.3. Parameters of the Calculation Program The lighting simulation program used to determine the dynamic daylight metrics and the energy saving in electric lighting is DaySim 3.1. This software is based on the Radiance engine, developed by the Building Technologies Department at the Lawrence Berkeley National Laboratory, and validated by several studies [ 40 , 41 ]. DaySim was designed to achieve a more accurate calculation than the initial form of Radiance, defining the current metrics according to modern sky definitions [ 26 ]. Like the Radiance engine, DaySim has been validated by several researchers [ 42 , 43 ] using CIE test cases [ 44 ]. Table 3shows the calculation parameters used by this program in this study. Table 3. Parameters of the calculation program. Ambient Bounces 7 Ambient Divisions 1500 Ambient Super-samples 100 Ambient Resolution 300 Ambient Accuracy 0.05 Limit Reflection 10 Specular Threshold 0.0000 Specular Jitter 1.0000 Limit Weight 0.0040 Direct Jitter 0.0000 Direct Sampling 0.2000 Direct Relays 2 Direct Pretest Density 512 Energies 2018,11, 3143 8 of 27 An interval of 5 min is considered for the measuring of the illuminance values during the calculation period. The illuminance requirements and the occupancy hours are described below. 2.4. Calculation Metrics 2.4.1. Daylight Metrics and Conditions Two dynamic metrics were assessed in the room models defined above. The first of these was daylight autonomy (DA), a concept conceived by the Association Suisse des Electriciens [ 45 ] and redefined by Reinhart et al. [ 46 ]. This metric is defined as the percentage of the year when a minimum illuminance threshold is met by daylight alone so that the higher the daylight autonomy, the lower the power consumption in electric lighting. This metric can be defined as Equation (1): DA =∑iw fi·ti ∑iti ∈[0,1]w fi=(1i f ED≥EL 0i f ED<EL (1) where DA is daylight autonomy, ti is the occupied time in a year, w fi is the weighting factor which depends on the illuminance threshold, ED is the daylight illuminance measured at a given point, and ELis the illuminance threshold. The second dynamic metric is continuous daylight autonomy (DAC) which represents the percentage of the year when a minimum illuminance threshold is met by daylight alone, considering a partial credit linearly to values below the threshold defined [ 47 ]. Therefore, this metric can be expressed as Equation (2): DAC =∑iw fi·ti ∑iti ∈[0,1]w fi=(1i f ED≥EL ED/ELi f ED<EL (2) where DAC is continuous daylight autonomy, ti is the occupied time in a year, w fi is the weighting factor which depends on the illuminance threshold, ED is the daylight illuminance measured at a given point, and ELis the illuminance threshold. According to the previous formulae, the dynamic metrics are calculated depending on the weather conditions which define daylight illuminance, the illuminance threshold and the occupancy time. The three locations selected for this study represent a wide range of weather conditions and latitudes from 40 to 60 degrees. As with the electric lighting design, the illuminance threshold is 500 lx, a standard value for offices according to EN 12461-1:2012 [ 34 ]. Finally, occupancy begins at 8.00 am and finishes at 5.00 pm, following the typical schedule for office rooms. As can be deduced from the metrics described above, the assessment of daylight autonomy can determine the percentage of use of the lighting system in this time frame and thus, the power consumption in electric lighting using an On/Off control system. Moreover, the analysis of continuous daylight autonomy can ascertain not only the on-time of the electric lighting, but also the amount of light provided when it is turned on and thus, the power consumption in electric lighting when a dimming system is in place. Both metrics therefore are useful in determining the energy efficiency produced by the smart controls proposed in this study. 2.4.2. Cost-Effectiveness Metrics and Conditions This study uses the net present value (NPV) indicator to evaluate and compare the economic profitability of the proposed lighting control hypotheses. This metric establishes the economic return on the investment for a given number of years [ 48 ], as the following expressions (Equations (3)–(5)) show: NPV =−I0+ n ∑ i=1 FBi (1+Dr)i(3) Energies 2018,11, 3143 9 of 27 FBi=Es·EC·AGRE(4) AGRE=ECi=n ECi=11 n −1 (5) where: •I0is the Initial Investment cost, shown in Table 2for each hypothesis; • nis the Project horizon (a maximum of 10 years is considered a reasonable lifetime for LED lighting systems for office buildings (50,000 h); •iis the Year of the study; • FB i is the Flow of benefits obtained in the year i(due to the savings in the annual electricity bill); • D r is the Discount rate (a 2.1% conservative value is assumed, as considered by energy companies [49,50]); •Esis Annual electric energy saving for each hypothesis, in kW·h; •ECiis the Annual electric energy cost for each country [51], in €/(kW·h); •AGREis the Annual Growth Rate of the electric energy cost for each country. The flow of benefits (FB i ), affected by the national price of electric energy and its fluctuations, is difficult to predict, especially when a period of 10 years is considered (NPV 10 ). This is because the Annual electric energy cost used here was that of 2016 and the Annual Growth Rate of the electric energy cost (AGR E ) was calculated from the annual electricity prices from 2005 to 2016 for each country under study, according to EUROSTAT [ 51 ]. Both EC and EC i for each country under study are shown in Table 4: Table 4. Annual electric cost and its annual growth rate (AGR) in Sweden, United Kingdom and Spain. Sweden (Stockholm) United Kingdom (London) Spain (Madrid) Annual electric energy cost in 2016 (EC 2016 ) 0.1894 €/(kW·h) 0.1951 €/(kW·h) 0.2185 €/(kW·h) Annual Growth Rate 2005–2016 (AGRE) 2.8% 7.5% 6.5% Thus, the economic viability of the investments performed can be evaluated according to NPV value; the higher the NPV, the better the return on the lighting control system. It is also interesting to note the year in which the NPV value changes from negative to positive, as this shows when the investment starts to yield profits. This time indicator is the payback period (PP) which is assessed in this research to determine the suitability of the smart controls proposed. 3. Validation of the Calculation Program and the Dynamic Metrics The calculation program and both dynamic metrics were validated, given that a computational simulation is not reliable until it has been compared to a real model. For this purpose, an existing test cell, located in Seville (Spain) was used as a reference [52]. 3.1. Characteristics of the Test Cell for Validation Process The room selected for this validation process is one of the test cells of TEP-130 research group [ 53 ], located in Seville (Spain) and facing south, in order to optimize rehabilitation solutions on façades and windows in the Mediterranean area. The real model, which generates the predictive results for DA and DAC, is defined from the test cell characteristics, as a room 2.40 m wide by 3.20 m deep by 2.70 m high. The entire enclosure, including the floor and the roof, is built using high density sandwich panels with a combined thickness of 460 mm, colored in white and screwed to a steel frame structure. The wall facing south has a window 116 cm wide by 108 cm high, with aluminum sliding frame and double glazing (two 4 mm glass and an 8 mm air space) with a solar factor of 0.75. A conservation factor of 0.8 is considered for Energies 2018,11, 3143 16 of 27 Table 8. Average power consumption in W/m2of different lighting smart controls in London. AVERAGE POWER CONSUMPTION (W/m2) London Depth Window-to-Façade Ratio Reflec-tance Case Study 0 Case Study 1 Case Study 2 Case Study 3 3.0 m 30% High 3.8 3.8 2.5 2.5 3.0 m 60% High 2.8 2.8 1.9 1.9 3.0 m 90% High 2.3 2.3 1.5 1.5 3.0 m 30% Low 5.6 5.6 3.6 3.6 3.0 m 60% Low 4.0 4.0 2.6 2.6 3.0 m 90% Low 3.3 3.3 2.2 2.2 6.0 m 30% High 7.8 6.4 4.8 4.0 6.0 m 60% High 4.9 4.2 3.1 2.7 6.0 m 90% High 3.9 3.4 2.5 2.2 6.0 m 30% Low 9.7 8.9 7.3 6.0 6.0 m 60% Low 8.7 6.9 5.1 4.2 6.0 m 90% Low 6.4 5.2 4.0 3.3 9.0 m 30% High 8.1 7.1 6.7 5.6 9.0 m 60% High 7.9 6.5 4.9 4.0 9.0 m 90% High 6.4 5.3 3.8 3.2 9.0 m 30% Low 9.7 9.1 8.7 7.3 9.0 m 60% Low 9.7 8.2 7.7 6.2 9.0 m 90% Low 9.7 7.9 6.8 5.4 As seen in Table 8and deduced from the previous trial, the deep rooms need higher power consumption for electric lighting, since daylight cannot reach the back of the room. In this case, window size is decisive in reducing the power consumption, given that large windows consume between 20% and 50% less power than small windows. Comparison of Tables 6and 8shows that the room model in the London location requires approximately 13% more energy than the Stockholm location. The difference between the locations only tends to converge for deep rooms, given that the daylight is not sufficient to light the entire venue in either case. It can therefore be deduced that weather conditions are more significant than latitude in the calculation of power consumption in electric lighting. As in the previous case, smart controls notably reduce power consumption in electric lighting. The dimming controls save nearly 30% of energy compared to the typical On/Off controls, while the dimming controls with separate rows reduce power consumption by up to 50%. Following on from Figure 5and Table 8, Table 9shows the annual energy saving, based on proposed room dimensions, window size, surface reflectances and smart controls. As before, the annual energy saving is calculated comparing the average power consumption of the smart controls to that of the conventional On/Off control. Table 9. Annual energy saving in kWh of different lighting smart controls in London. ANNUAL ENERGY SAVING (kWh) London Width 12.0 m 8.0 m 4.0 m 12.0 m 8.0 m 4.0 m 12.0 m 8.0 m 4.0 m Depth Window-to-Façade Ratio Reflec-tance Case Study 3: Dimming Control with Two Separate Rows Case Study 2: Dimming Control for All Luminaires Case Study 1: On/Off Control with Two Separate Rows 3.0 m 30% High - - - 121 81 40 - - - 3.0 m 60% High - - - 83 56 28 - - - 3.0 m 90% High - - - 68 45 23 - - - 3.0 m 30% Low - - - 191 127 64 - - - 3.0 m 60% Low - - - 127 85 42 - - - 3.0 m 90% Low - - - 100 67 33 - - - 6.0 m 30% High 705 470 235 561 374 187 250 167 83 6.0 m 60% High 409 273 136 333 222 111 121 81 40 6.0 m 90% High 311 207 104 258 172 86 98 66 33 6.0 m 30% Low 700 467 233 455 303 152 155 103 52 6.0 m 60% Low 855 570 285 673 448 224 345 230 115 6.0 m 90% Low 582 388 194 455 303 152 218 145 73 9.0 m 30% High 705 470 235 386 258 129 265 177 88 9.0 m 60% High 1076 717 359 841 561 280 386 258 129 9.0 m 90% High 902 601 301 727 485 242 318 212 106 9.0 m 30% Low 664 442 221 273 182 91 173 115 58 9.0 m 60% Low 991 661 330 573 382 191 427 285 142 9.0 m 90% Low 1209 806 403 818 545 273 518 345 173 Energies 2018,11, 3143 17 of 27 As seen in Table 9, converging with the results shown for the Stockholm location, the energy saving is higher for rooms with small windows, except in the case of deep rooms. As above, it can be concluded that a room with a small window can compensate for power consumption using a dimming control with two separate rows (Case study 3), compared to other rooms with larger windows and less efficient smart controls (case studies 1 and 2). As deduced from Figure 5and Table 6above, owing to worse weather conditions the power consumption for London is higher than that for Stockholm. However, comparison of Tables 7and 9 shows that the energy saving in London is higher for narrow rooms. In fact, rooms 3.00 m deep increase energy saving by almost 20% for dimming controls compared to Stockholm. The opposite occurs in the case of deep rooms, as the energy savings of dimming controls are slightly higher in Stockholm. 4.3. Quantification of Power Consumption in Madrid As above, Figure 6describes the cross sections for all room models for the Madrid location, defining the dynamic daylight metrics and the average power consumption at the central axis. This figure follows the same structure as the previous ones. The identifier for each room, defined in Table 1, is on the upper-right side of each section. Table 10 shows the average power consumption of lighting smart controls in W/m 2 , based on the case studies. In line with previous results Table 10 shows that the power consumption in electric lighting for deep rooms with conventional On/Off controls is similar for all locations, irrespective of weather conditions and the reflectance of the inner surfaces. This is because daylight cannot reach the illuminance threshold in the entire room. Therefore, the use of smart controls is even more advantageous in locations with clear skies. Following the analysis and results of Table 10, except for deep rooms with a low reflectance, large windows (window-to-façade ratio of 90%) consume between 40% and 75% less in lighting than small windows (window-to-façade ratio of 30%). It can be deduced that the impact of a large window on energy efficiency is higher for sites with better weather conditions, and a higher sky luminance. Table 10. Average power consumption in W/m2of different lighting smart controls in Madrid. AVERAGE POWER CONSUMPTION (W/m2) Madrid Depth Window-to-Façade Ratio Reflec-tance Case Study 0 Case Study 1 Case Study 2 Case Study 3 3.0 m 30% High 1.7 1.7 1.1 1.1 3.0 m 60% High 1.1 1.1 0.7 0.7 3.0 m 90% High 0.9 0.9 0.6 0.6 3.0 m 30% Low 3.0 3.0 1.7 1.7 3.0 m 60% Low 1.7 1.7 1.1 1.1 3.0 m 90% Low 1.3 1.3 1.0 1.0 6.0 m 30% High 6.1 4.5 2.7 2.1 6.0 m 60% High 2.3 1.9 1.3 1.1 6.0 m 90% High 1.6 1.4 1.0 0.9 6.0 m 30% Low 9.7 7.7 5.7 4.1 6.0 m 60% Low 6.1 4.3 2.7 2.0 6.0 m 90% Low 3.6 2.7 1.8 1.5 9.0 m 30% High 8.1 6.4 5.7 4.3 9.0 m 60% High 6.4 4.8 2.8 2.2 9.0 m 90% High 3.8 2.9 1.9 1.5 9.0 m 30% Low 9.7 8.4 8.2 6.2 9.0 m 60% Low 9.7 7.3 6.3 4.7 9.0 m 90% Low 9.7 7.1 4.8 3.6 Energies 2018,11, 3143 18 of 27 Energies 2018, 11, x FOR PEER REVIEW 18 of 27 Figure 6. Dynamic daylight performance metrics and average power consumption according to different smart controls for room models located in Madrid. Table 10. Average power consumption in W/m2 of different lighting smart controls in Madrid. AVERAGE POWER CONSUMPTION (W/m2) Madrid Depth Window-to- Façade Ratio Reflectance Case Study 0 Case Study 1 Case Study 2 Case Study 3 3.0 m 30% High 1.7 1.7 1.1 1.1 3.0 m 60% High 1.1 1.1 0.7 0.7 3.0 m 90% High 0.9 0.9 0.6 0.6 3.0 m 30% Low 3.0 3.0 1.7 1.7 3.0 m 60% Low 1.7 1.7 1.1 1.1 3.0 m 90% Low 1.3 1.3 1.0 1.0 6.0 m 30% High 6.1 4.5 2.7 2.1 6.0 m 60% High 2.3 1.9 1.3 1.1 6.0 m 90% High 1.6 1.4 1.0 0.9 6.0 m 30% Low 9.7 7.7 5.7 4.1 Figure 6. Dynamic daylight performance metrics and average power consumption according to different smart controls for room models located in Madrid. From the above, the room model in the Madrid location consumes almost 40% less power consumption than London and almost 35% less than Stockholm. As seen above, the smart controls are decisive in controlling power consumption in electric lighting. Extending this statement to all the locations studied, the dimming controls (Case study 2) produce an energy saving of close to 30% compared to the typical On/Off controls, while the dimming controls with separate rows (Case study 3) reduce power consumption by up to 55%. Moreover, the On/Off lighting control with separate rows (Case study 1) can reduce power consumption by up to 20% compared to the On/Off system with one row for all luminaires. In accordance with the results of Figure 6and Table 10, the annual energy saving is determined in Table 11, based on the proposed room dimensions, window size, surface reflectances and smart controls. As above, the annual energy saving is calculated by comparing the average power consumption of the smart controls proposed to that produced by the conventional On/Off control. As seen in Table 11 and previously, energy saving is higher for rooms with small windows, due to the high dependence on electric lighting. Deep rooms are an exception, as the conventional On/Off control is almost always on, regardless of window size. As in the cases above, a room with a small Energies 2018,11, 3143 19 of 27 window can save more energy than other rooms with larger windows and less efficient smart controls by using dimming control with two separate rows. Table 11. Annual energy saving in kWh of different lighting smart controls in Madrid. ANNUAL ENERGY SAVING (kWh) Madrid Width 12.0 m 8.0 m 4.0 m 12.0 m 8.0 m 4.0 m 12.0 m 8.0 m 4.0 m Depth Window-to-Façade Ratio Reflec-tance Case Study 3: Dimming Control with Two Separate Rows Case Study 2: Dimming Control for All Luminaires Case Study 1: On/Off Control with Two Separate Rows 3.0 m 30% High - - - 61 40 20 - - - 3.0 m 60% High - - - 30 20 10 - - - 3.0 m 90% High - - - 23 15 8 - - - 3.0 m 30% Low - - - 127 85 42 - - - 3.0 m 60% Low - - - 55 36 18 - - - 3.0 m 90% Low - - - 27 18 9 - - - 6.0 m 30% High 750 500 250 636 424 212 303 202 101 6.0 m 60% High 227 152 76 197 131 66 76 51 25 6.0 m 90% High 136 91 45 121 81 40 45 30 15 6.0 m 30% Low 1045 697 348 745 497 248 373 248 124 6.0 m 60% Low 764 509 255 636 424 212 345 230 115 6.0 m 90% Low 400 267 133 327 218 109 173 115 58 9.0 m 30% High 1068 712 356 682 455 227 477 318 159 9.0 m 60% High 1174 783 391 1000 667 333 447 298 149 9.0 m 90% High 644 429 215 545 364 182 242 162 81 9.0 m 30% Low 973 648 324 436 291 145 382 255 127 9.0 m 60% Low 1409 939 470 955 636 318 673 448 224 9.0 m 90% Low 1727 1152 576 1391 927 464 745 497 248 A comparison of Tables 7,9and 11 shows that the energy saving in Madrid is noticeably lower than in London or Stockholm for rooms 3.00 m deep, achieving an average of 40% less, based on the selection of a base case (system 0) that is auto-switched by users. However, the deeper rooms in Madrid can save up to 40% energy compared to other sites. In conclusion, considering rooms between 6.00 m and 9.00 m deep, the greater the sky luminance, the higher the energy savings with lighting smart controls. Moreover, the opposite of this statement is true for rooms 6.00 m deep or less. 5. Analysis of Payback Period and Net Present Value 5.1. Cost Effectiveness in Stockholm According to Section 2, the profitability of the different hypotheses for lighting control in Stockholm are compared using the payback period (PP) and the 10-year-investment NPV (NPV10). Table 12 shows that adequate PPs are achieved for the three control systems in rooms 6.00 m deep or more (PP average value of 4.5 years), but these are not recommended for rooms 3.00 m deep (PP average value of 18.6 years). When width is analyzed in rooms 6.00 m deep or more, it is worth noting that PP decreases for rooms 8.00 m wide or more, (PP average value of 3.3 years). Increasing the technical complexity of the control system extends the payback period, as the initial investment costs correlate to complexity (PP average value of 2.9, 3.0 and 3.9 years for cases 1, 2 and 3 respectively). Energies 2018,11, 3143 20 of 27 Table 12. Payback period according to different lighting smart controls in Stockholm. PAYBACK PERIOD Stockholm Width 12.0 m 8.0 m 4.0 m 12.0 m 8.0 m 4.0 m 12.0 m 8.0 m 4.0 m Depth Window-to-Façade Ratio Reflec-tance Case Study 3: Dimming Control with Two Separate Rows Case Study 2: Dimming Control for all Luminaires Case Study 1: On/Off Control with Two Separate Rows 3.0 m 30% High - - - 9 years 13 years 24 years --- 3.0 m 60% High - - - 13 years 19 years 36 years --- 3.0 m 90% High - - - 17 years 24 years 45 years --- 3.0 m 30% Low - - - 6 years 9 years 16 years --- 3.0 m 60% Low - - - 9 years 13 years 24 years --- 3.0 m 90% Low - - - 11 years 16 years 31 years --- 6.0 m 30% High 3 years 4 years 7 years 2 years 3 years 5 years 2 years 3 years 5 years 6.0 m 60% High 5 years 8 years 15 years 4 years 5 years 10 years 5 years 6 years 12 years 6.0 m 90% High 7 years 10 years 19 years 5 years 7 years 13 years 5 years 8 years 14 years 6.0 m 30% Low 3 years 4 years 7 years 2 years 3 years 5 years 2 years 3 years 5 years 6.0 m 60% Low 3 years 4 years 7 years 2 years 3 years 5 years 2 years 3 years 5 years 6.0 m 90% Low 4 years 5 years 10 years 3 years 4 years 7 years 3 years 4 years 7 years 9.0 m 30% High 3 years 3 years 6 years 2 years 3 years 6 years 2 years 2 years 4 years 9.0 m 60% High 2 years 3 years 5 years 2 years 2 years 4 years 2 years 2 years 4 years 9.0 m 90% High 3 years 4 years 8 years 2 years 3 years 5 years 2 years 3 years 5 years 9.0 m 30% Low 3 years 4 years 7 years 3 years 5 years 9 years 2 years 2 years 4 years 9.0 m 60% Low 2 years 3 years 5 years 2 years 2 years 4 years 1 years 2 years 3 years 9.0 m 90% Low 2 years 2 years 4 years 1 years 2 years 3 years 1 years 2 years 3 years Table 13 shows that adequate NPV10 is obtained in the same way for rooms 6.00 m deep or more (average NPV10 of 497.90 € compared to − 44.83 € in the case of 3.00 m deep), and especially for rooms 8.00 m wide or more (average NPV10 of 668.22 € ). However, unlike the payback period, the NPV10 shows that the more complex the control system in larger rooms, the greater the economic benefits obtained after 10 years. Using a smart control instead of an On/Off manual control with two separate rows (case 1) ultimately provides an economic saving of 73% in the case of dimming control for all luminaires (case 2), and 126% for smart control with two separate rows (case 3), as energy savings are greater in the long term despite their higher initial investment costs. As with power consumption, the profitability of the smart systems is higher for rooms at least 8.00 m wide and small windows, except in the case of deep rooms with dark surfaces. For example, all NPV10 values of Case study 3 with small windows (window-to-façade ratio of 30%) are equal to or greater than the NPV10 values of Case study 1 with the largest windows (window-to-façade ratio of 90%). Table 13. Net present value in 10 years according to different lighting smart controls in Stockholm. NET PRESENT VALUE IN 10 YEARS Stockholm Width 12.0 m 8.0 m 4.0 m 12.0 m 8.0 m 4.0 m 12.0 m 8.0 m 4.0 m Depth Window-to-Façade Ratio Reflec-tance Case Study 3: Dimming Control with Two Separate Rows Case Study 2: Dimming Control for All Luminaires Case Study 1: On/Off Control with Two Separate Rows 3.0 m 30% High - - - 34 €−33 €−101 €- - - 3.0 m 60% High - - - −38 €−82 €−125 €- - - 3.0 m 90% High - - - −67 €−101 €−135 €- - - 3.0 m 30% Low - - - 145 €40 €−64 €- - - 3.0 m 60% Low - - - 40 €−29 €−99 €- - - 3.0 m 90% Low - - - −12 €−64 €−116 €- - - 6.0 m 30% High 1026 €581 €137 €933 €566 €199 €419 €255 €90 € 6.0 m 60% High 330 €117 €−95 €353 €179 €5€115 €52 €−11 € 6.0 m 90% High 185 €21 €−143 €237 €102 €−33 €86 €32 €−21 € 6.0 m 30% Low 1240 €724 €208 €875 €527 €179 €465 €286 €106 € 6.0 m 60% Low 1066 €608 €150 €910 €550 €191 €483 €297 €112 € 6.0 m 90% Low 649 €330 €11 €562 €319 €75 €291 €170 €48 € 9.0 m 30% High 1272 €746 €219 €788 €469 €150 €578 €361 €144 € 9.0 m 60% High 1780 €1084 €388 €1527 €962 €397 €680 €429 €177 € 9.0 m 90% High 1011 €572 €132 €875 €527 €179 €404 €245 €86 € 9.0 m 30% Low 1153 €666 €179 €458 €249 €40 €500 €309 €117 € 9.0 m 60% Low 1797 €1095 €394 €1136 €701 €266 €900 €576 €251 € 9.0 m 90% Low 2301 €1432 €562 €1762 €1119 €475 €1039 €668 €297 € Energies 2018,11, 3143 21 of 27 5.2. Cost Effectiveness in London The PP for the three control systems for the London room model is shown in Table 14. Considering the sky conditions and as shown in Table 4, in general PP is 33.5% lower for all the London hypotheses since both the annual electric energy cost in 2016 (EC2016) and the annual growth rate (AGRE) in the United Kingdom are higher than in Sweden. Nevertheless, the use of these three system controls is still advantageous to rooms 6.00 m deep or more (PP average value of 3.3 years compared to 10.1 years in the case of 3.00 m deep), except when they are 12.00 m wide or more (PP average value of 6.7 years). As in the case of Stockholm, the wider the deep rooms, the lower the PP for all the control systems (PP average value of 2.5 years in rooms 8.00 m wide or more), resulting in an average PP decrease of 23.8% compared to Stockholm. As seen in Stockholm, Table 15 shows that adequate NPV10 is related to depths equal to or greater than 6.00 m (average NPV10 of 693.32 € , a 39.3% increase compared to Stockholm), especially for widths of 8.00 m or more (average NPV10 of 904.60 € , an increase of 35.4%). In the same way, the most complex control systems save most after 10 years, as the average economic saving for smart control systems compared to case 1 is 88% for case 2 and 156% for case 3. These economic results are more significant than the Stockholm ones (a NPV10 increase of 24.3%, 34.8% and 40.7% in the three cases) due both to the higher electric energy cost in the United Kingdom and the higher annual energy saving of lighting control systems in London. Table 14. Payback period according to different lighting smart controls in London. PAYBACK PERIOD London Width 12.0 m 8.0 m 4.0 m 12.0 m 8.0 m 4.0 m 12.0 m 8.0 m 4.0 m Depth Window-to-Façade Ratio Reflec-tance Case Study 3: Dimming Control with Two Separate Rows Case Study 2: Dimming Control for All Luminaires Case Study 1: On/Off Control with Two Separate Rows 3.0 m 30% High - - - 6 years 8 years 13 years --- 3.0 m 60% High - - - 8 years 11 years 17 years --- 3.0 m 90% High - - - 9 years 12 years 20 years --- 3.0 m 30% Low - - - 4 years 6 years 10 years --- 3.0 m 60% Low - - - 6 years 8 years 13 years --- 3.0 m 90% Low - - - 7 years 9 years 15 years --- 6.0 m 30% High 2 years 3 years 6 years 2 years 2 years 4 years 2 years 2 years 4 years 6.0 m 60% High 4 years 5 years 9 years 3 years 4 years 6 years 3 years 4 years 7 years 6.0 m 90% High 5 years 6 years 11 years 3 years 4 years 8 years 3 years 5 years 8 years 6.0 m 30% Low 2 years 3 years 6 years 2 years 3 years 5 years 2 years 3 years 6 years 6.0 m 60% Low 2 years 3 years 5 years 2 years 2 years 4 years 1 years 2 years 3 years 6.0 m 90% Low 3 years 4 years 7 years 2 years 3 years 5 years 2 years 2 years 4 years 9.0 m 30% High 2 years 3 years 6 years 2 years 3 years 6 years 2 years 2 years 4 years 9.0 m 60% High 2 years 2 years 4 years 1 years 2 years 3 years 1 years 2 years 3 years 9.0 m 90% High 2 years 3 years 5 years 1 years 2 years 3 years 1 years 2 years 3 years 9.0 m 30% Low 3 years 3 years 6 years 3 years 4 years 7 years 2 years 3 years 5 years 9.0 m 60% Low 2 years 3 years 4 years 2 years 2 years 4 years 1 years 2 years 2 years 9.0 m 90% Low 2 years 2 years 4 years 1 years 2 years 3 years 1 years 1 years 2 years In line with the Stockholm results, the lighting system of a room with a small window can be more economical using a dimming control with two separate rows (Case study 3), than one installed in rooms with larger windows and less efficient control systems (case studies 1 and 2). Energies 2018,11, 3143 22 of 27 Table 15. Net present value in 10 years according to different lighting smart controls in London. NET PRESENT VALUE IN 10 YEARS London Width 12.0 m 8.0 m 4.0 m 12.0 m 8.0 m 4.0 m 12.0 m 8.0 m 4.0 m Depth Window-to-Façade Ratio Reflec-tance Case Study 3: Dimming Control with Two Separate Rows Case Study 2: Dimming Control for All Luminaires Case Study 1: On/Off Control with Two Separate Rows 3.0 m 30% High - - - 158 €60 €−39 €- - - 3.0 m 60% High - - - 66 €−2€−69 €- - - 3.0 m 90% High - - - 29 €−26 €−82 €- - - 3.0 m 30% Low - - - 328 €173 €18 €- - - 3.0 m 60% Low - - - 173 €70 €−34 €- - - 3.0 m 90% Low - - - 107 €25 €−56 €- - - 6.0 m 30% High 1453 €881 €309 €1229 €773 €318 €554 €351 €148 € 6.0 m 60% High 733 €401 €69 €675 €404 €134 €240 €142 €43 € 6.0 m 90% High 493 €241 €−11 €490 €281 €72 €185 €105 €25 € 6.0 m 30% Low 1442 €873 €305 €970 €601 €232 €322 €196 €71 € 6.0 m 60% Low 1818 €1124 €431 €1502 €956 €409 €787 €506 €226 € 6.0 m 90% Low 1154 €682 €209 €970 €601 €232 €477 €299 €122 € 9.0 m 30% High 1453 €881 €309 €804 €490 €177 €591 €376 €160 € 9.0 m 60% High 2357 €1484 €610 €1912 €1229 €546 €886 €573 €259 € 9.0 m 90% High 1933 €1201 €469 €1635 €1044 €454 €720 €462 €203 € 9.0 m 30% Low 1353 €814 €276 €527 €306 €84 €366 €226 €85 € 9.0 m 60% Low 2151 €1346 €541 €1258 €793 €328 €986 €639 €292 € 9.0 m 90% Low 2682 €1700 €718 €1856 €1192 €527 €1207 €787 €366 € 5.3. Cost Effectiveness in Madrid The PP results for Madrid are shown in Table 16. As in Table 4, the annual electric cost in 2016 (EC2016) in Spain is the highest of the three locations under study and, although its annual growth rate is not the highest, the electric energy cost forecast obtained remains higher than in the United Kingdom and Sweden. Accordingly, the lighting smart controls promote greater economic savings. As in the previous locations studied, there is adequate profitability for the three lighting control systems installed in rooms 6.00 m deep or more (PP average value of 3.1 years compared to 17.5 years), especially when the rooms are at least 8.00 m wide (PP average value of 2.3 years in the case of 3.00 m deep). There is also an average PP decrease of 28.5% and 6.1% compared to Stockholm and London, respectively. Table 16. Payback period according to different lighting smart controls in Madrid. PAYBACK PERIOD Madrid Width 12.0 m 8.0 m 4.0 m 12.0 m 8.0 m 4.0 m 12.0 m 8.0 m 4.0 m Depth Window-to-Façade Ratio Reflec-tance Case Study 3: Dimming Control with Two Separate Rows Case Study 2: Dimming Control for All Luminaires Case Study 1: On/Off Control with Two Separate Rows 3.0 m 30% High - - - 9 years 12 years 19 years --- 3.0 m 60% High - - - 15 years 19 years 30 years --- 3.0 m 90% High - - - 18 years 23 years 35 years --- 3.0 m 30% Low - - - 5 years 7 years 11 years --- 3.0 m 60% Low - - - 9 years 13 years 21 years --- 3.0 m 90% Low - - - 16 years 21 years 32 years --- 6.0 m 30% High 2 years 3 years 5 years 1 years 2 years 3 years 1 years 2 years 3 years 6.0 m 60% High 5 years 7 years 12 years 3 years 5 years 8 years 3 years 5 years 8 years 6.0 m 90% High 8 years 11 years 18 years 5 years 7 years 12 years 5 years 7 years 12 years 6.0 m 30% Low 2 years 2 years 4 years 1 years 2 years 3 years 1 years 1 years 2 years 6.0 m 60% Low 2 years 3 years 5 years 1 years 2 years 3 years 1 years 1 years 2 years 6.0 m 90% Low 3 years 4 years 8 years 2 years 3 years 5 years 2 years 2 years 4 years 9.0 m 30% High 2 years 2 years 4 years 1 years 2 years 3 years 1 years 1 years 2 years 9.0 m 60% High 1 years 2 years 3 years 1 years 1 years 2 years 1 years 1 years 2 years 9.0 m 90% High 2 years 3 years 5 years 2 years 2 years 4 years 1 years 2 years 3 years 9.0 m 30% Low 2 years 2 years 4 years 2 years 2 years 4 years 1 years 1 years 2 years 9.0 m 60% Low 1 years 2 years 3 years 1 years 1 years 2 years 1 years 1 years 1 years 9.0 m 90% Low 1 years 1 years 2 years 1 years 1 years 2 years 1 years 1 years 1 years As in the other two locations, Table 17 shows that the use of lighting control systems starts to be profitable for rooms 6.00 m deep or more (average NPV10 of 940.22 € , an increase of 88.8% and Energies 2018,11, 3143 23 of 27 35.6% compared to Stockholm and London, respectively), obtaining the highest economic savings for rooms 8.00 m wide or more (average NPV10 of 1209.58 € , an increase of 81.0% and 33.7%, respectively), especially for rooms 9.00 m deep. In the same way, the greatest economic savings after 10 years are obtained when smart systems are used, with an average NPV10 improvement compared to case 1, of 74% for case 2 and 123% for case 3. Thus, given that the Spanish electric energy cost forecast is the highest of the three locations under study, and that Madrid is the location with the highest annual energy saving in the deepest rooms, the economic saving increases using the systems from cases 2 and 3 in these wide, deep rooms was 82.6% and 79.4% compared to Stockholm, and 35.4% and 27.5% compared to London, respectively. As shown in the case of Stockholm and London, using smart systems in rooms at least 8.00 m wide with small windows is more profitable than installing manual On/Off control systems with two separate rows, except in the case of deep rooms with dark surfaces. According to the results for the three locations above, it can be concluded that for rooms at least 6.00 m deep and 8.00 m wide, the more complex the control system, the greater the profitability obtained after 10 years. Moreover, due to the higher initial investment costs of the smart control systems, the simpler the control system, the sooner the cost is amortized. Table 17. Net present value in 10 years according to different lighting smart controls in Madrid. NET PRESENT VALUE IN 10 YEARS Madrid NPV Width 12.0 m 8.0 m 4.0 m 12.0 m 8.0 m 4.0 m 12.0 m 8.0 m 4.0 m Depth Window-to-Façade Ratio Reflec-tance Case Study 3: Dimming Control with Two Separate Rows Case Study 2: Dimming Control for All Luminaires Case Study 1: On/Off Control with Two Separate Rows 3.0 m 30% High - - - 36 €−17 €−70 €- - - 3.0 m 60% High - - - −43 €−70 €−96 €- - - 3.0 m 90% High - - - −63 €−83 €−103 €- - - 3.0 m 30% Low - - - 210 €99 €−12 €- - - 3.0 m 60% Low - - - 20 €−27 €−75 €- - - 3.0 m 90% Low - - - −51 €−75 €−99 €- - - 6.0 m 30% High 1716 €1063 €410 €1540 €986 €432 €745 €482 €218 € 6.0 m 60% High 350 €152 €−45 €392 €221 €49 €152 €86 €20 € 6.0 m 90% High 113 €−6€−125 €194 €89 €−17 €72 €33 €−7€ 6.0 m 30% Low 2488 €1577 €667 €1825 €1176 €527 €927 €603 €278 € 6.0 m 60% Low 1752 €1087 €422 €1540 €986 €432 €856 €555 €255 € 6.0 m 90% Low 802 €453 €105 €733 €448 €163 €405 €255 €104 € 9.0 m 30% High 2547 €1617 €687 €1659 €1065 €471 €1201 €785 €369 € 9.0 m 60% High 2824 €1802 €779 €2490 €1619 €748 €1121 €732 €343 € 9.0 m 90% High 1439 €878 €317 €1303 €828 €353 €587 €376 €165 € 9.0 m 30% Low 2298 €1451 €604 €1018 €638 €258 €951 €619 €286 € 9.0 m 60% Low 3438 €2211 €984 €2371 €1540 €709 €1711 €1125 €540 € 9.0 m 90% Low 4269 €2765 €1261 €3511 €2300 €1089 €1901 €1252 €603 € 6. Conclusions Building design is currently moving towards a new age of efficient construction, using cutting-edge technologies to promote higher energy efficiency. In the field of lighting, the use of smart controls is key to reducing electricity consumption. Therefore, dynamic daylight metrics are useful tools for determining the energy savings obtained using different smart controls. As deduced from Section 3on the quantification of power consumption, except for deep rooms with low reflectance, large windows (window-to-façade ratio of 90%) save between 40% and 60% more energy compared to small windows (window-to-façade ratio of 30%). Moreover, medium windows (window-to-façade ratio of 60%) use 20–40% less energy in lighting than small windows. The impact of a large window on energy efficiency is higher in sites with better weather conditions. Smart controls noticeably reduce power consumption in electric lighting. Dimming controls (Case study 2) in particular save almost 30% energy compared to the conventional On/Off control (Case study 0). In addition, the switching control with separate rows (Case study 1) saves up to 20% energy. Combining both strategies, the dimming controls with separate rows of luminaires (Case study 3) Energies 2018,11, 3143 24 of 27 save between 35% and 55% more than the typical On/Off control (Case study 0). This applies to all latitudes studied. Using smart controls, energy saving is higher for cases with small windows due to the dependence on electric lighting. Deep rooms, where the typical switch control is almost always on, are the exception, regardless of window size. Moreover, smaller windows, which consume less energy in electric lighting, can compensate for this weak point using smart controls. In studying energy efficiency based on room depth, energy saving in Madrid is noticeably lower than in London or Stockholm for rooms 3.00 m deep, resulting in an average of 40% less than in the other locations. However, the deeper rooms in Madrid improved energy saving by almost 40% compared to other sites. The results obtained for Madrid are also particularly unique due to the high illuminance caused by daylight and the high cost of energy in Spain. Therefore, it can be concluded that, considering a depth greater than 6.00 m, the greater the sky luminance, the higher the energy savings with lighting smart controls. The opposite is true of rooms 6.00 m deep or less. As explained in the methodology, the results obtained for Madrid could be extrapolated to the Mediterranean climate, while the conclusions for London and Stockholm could be assumed for other locations in Northern Europe. As seen in Section 4on the profitability of the proposed smart controls, suitable payback periods (PPs) can be achieved for the three control systems in rooms 6.00 m deep or more (PP average value from 3.1 to 4.5 years), but not for rooms 3.00 m deep (PP average value from 10.1 to 18.6 years) while in rooms 6.00 m deep or more and 8.00 m wide or more, the PP decreases noticeably (PP average value from 2.3 to 3.3 years). It is worth noting that according to the sky conditions and the electric energy cost considered, the best PP is observed for Madrid, while the least favorable value is seen for Stockholm. The final analysis is net present value (NPV10), used to determine the economic benefits obtained after 10 years due to the energy saving. Unlike payback periods, the NPV10 shows that in larger rooms, the more complex the control system, the greater the economic benefits obtained after 10 years. Using a dimming control (Case study 2) instead of a switch control with two separate rows (Case study 1) finally saves an average of between 73% and 88%, depending on the location. Moreover, using a dimming system with separate rows (Case study 3) rather than an On/Off control with separate rows (Case study 1) produces a final average benefit between 126% and 156%. In conclusion, the previous statements demonstrate the high energy efficiency and the economic profitability of lighting smart controls, and the undeniable usefulness of the dimming systems controlled by illuminance-meters in different scenarios. Author Contributions: Conceptualization, I.A.; methodology, I.A., M.Á.C. and S.D.-A.; investigation, I.A., M.Á.C. and S.D.-A.; writing—original draft preparation, I.A., M.Á.C. and C.M.; writing—review and editing, S.D.; supervision, I.A., M.Á.C. and S.D.; funding acquisition, I.A. Funding: The results presented were funded by the government of Spain through the research project: Efficient design for biodynamic lighting to promote the circadian rhythm in shift work centers (Ref BIA2017-86997-R). The authors wish to express their gratitude for all the technical and financial support provided. Acknowledgments: The authors are especially grateful to all those who collaborated in this research project, specially to Blas Lezo for the encouragement of this article. We would like to express a special acknowledgment to the Lighting Research Center at Rensselaer Polytechnic Institute (LRC)—USA, the Laboratório Nacional de Engenharia Civil (LNEC)—PT, and the School of Energy, Geoscience, Infrastructure and Society (EGIS) at Herito Watt University—UK—for their support and advice in the development of this work. Conflicts of Interest: The authors declare no conflicts of interest. References 1. Ryckaert, W.; Lootens, C.; Geldof, J.; Hanselaer, P. 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