Optimizing home energy systems through algorithmic techniques

 Corresponding author: Osagie-Bolaji Aimuamwosa Nathan Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0. Optimizing home energy systems through algorithmic techniques Osagie-Bolaji Aimuamwosa Nathan * and Idemudia Omorodion William Department of Electrical Engineering Technology, School of Engineering Edo State Polytechnic Usen. Global Journal of Engineering and Technology Advances, 2025, 23(03), 327-332 Publication history: Received on 26 May 2025; revised on 25 June 2025; accepted on 28 June 2025 Article DOI: https://doi.org/10.30574/gjeta.2025.23.3.0251 Abstract The growing demand for sustainable energy consumption, coupled with the rapid adoption of smart home technologies, has elevated the importance of optimizing residential energy systems. This paper provides a comprehensive survey of algorithmic techniques applied to home energy optimization, examining their underlying principles, strengths, limitations, and areas of applicability. The surveyed approaches are categorized into rule-based methods, optimizationdriven models, artificial intelligence (AI)-based strategies, and hybrid frameworks. By analyzing their effectiveness across diverse residential energy management scenarios, the study aims to present a structured overview of the stateof-the-art while highlighting emerging challenges and potential directions for future research in intelligent home energy optimization. Keywords: Smart Home; Home Energy Optimization; Rule-Based Algorithms; Optimization-Based Algorithms 1. Introduction The rapid urbanization and growing energy demand have necessitated the development of efficient energy management strategies, especially in residential sectors, which account for a significant portion of global electricity consumption. According to the International Energy Agency (IEA), residential buildings consumed approximately 22% of the world’s total final energy in 2022, highlighting the importance of optimization in domestic energy usage for sustainability and cost-effectiveness (International Energy Agency, 2023). The advent of smart grids, coupled with advancements in sensor technologies, Internet of Things (IoT), and artificial intelligence, has paved the way for Home Energy Management Systems (HEMS) that aim to optimize energy consumption while maintaining occupant comfort. Home energy optimization involves the intelligent control and scheduling of household appliances, integration of renewable energy sources such as solar photovoltaics (PV), and efficient use of energy storage systems. The primary objectives include reducing energy costs, minimizing peak demand, lowering carbon emissions, and improving overall energy efficiency. These goals are often conflicting, necessitating the use of sophisticated algorithms to achieve multi-objective optimization under dynamic constraints such as time-of-use pricing, occupant behavior, and weather conditions. Over the past decade, a wide range of algorithms ranging from traditional mathematical optimization techniques to modern machine learning and metaheuristic methods have been proposed to tackle the complex problem of energy optimization in homes. Deterministic methods such as Linear Programming (LP), Mixed-Integer Linear Programming (MILP), and Dynamic Programming (DP) have been widely used for their accuracy and reproducibility (Siano, 2014). However, their computational complexity and reliance on exact models limit their scalability and adaptability to real-world scenarios. On the other hand, heuristic and metaheuristic algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Simulated Annealing (SA) offer greater flexibility and robustness in handling non-linear, multi-modal optimization problems (Palensky and Dietrich, 2011a). Recent advancements in machine learning, particularly reinforcement learning (RL) and deep learning (DL), have opened new avenues for adaptive and data-driven energy optimization. These methods are particularly well-suited for capturing stochastic occupant behavior and dynamically learning optimal policies in real-time environments (Zhang et al., 2014). Global Journal of Engineering and Technology Advances, 2025, 23(03), 327-332 328 Hybrid approaches that combine the strengths of different algorithms have also gained attention for their potential to improve convergence speed and solution quality. This survey aims to provide a comprehensive overview of the stateof-the-art algorithms used for home energy optimization, categorizing them based on their methodological approach and application context. By analyzing the strengths, limitations, and applicability of each algorithm, this work intends to offer insights for researchers, developers, and policymakers seeking to design efficient and sustainable smart home systems. Figure 1 Smart Home Energy Flow and Control via Algorithmic Optimization 2. Categories of Algorithms for Home Energy Optimization 2.1. Rule-Based Algorithms Rule-based algorithms are among the most fundamental approaches used in energy management systems (EMS). They operate on predefined if-then logic rules, which are formulated based on expert knowledge or typical usage scenarios. These systems function by comparing sensor inputs or environmental conditions against fixed thresholds to make deterministic decisions. The two major rule-based algorithms are; 2.2. Time-of-Use (TOU) based control In this approach, appliance operation is scheduled according to predetermined electricity pricing tiers typically encouraging energy use during off-peak periods to minimize costs (Pipattanasomporn et al., 2012). For example, a water heater or washing machine may be set to run only during off-peak hours. 2.3. Priority scheduling Appliances are assigned fixed priorities based on their importance or criticality. Essential loads like refrigerators are always powered on, while less critical devices like dishwashers can be deferred or turned off during peak times (Vardakas et al., 2015). Global Journal of Engineering and Technology Advances, 2025, 23(03), 327-332 329 Table 1 Advantages and Disadvantages of Rule Based Algorithms Advantages Rule-Based Algorithms Disadvantages of Rule-Based Algorithms Rule-based systems are easy to design and implement since they rely on explicit logic and do not need training data or model learning. The Rule-based is static in nature difficult to adapt to variation in user behaviour, real-time electricity pricing and environmental changes unless rules are manually programmed. Rule-based systems use simple conditional checks, making them computationally lightweight and ideal for embedded systems with limited processing power Increase in devices, environmental conditions and usage scenarios can make the set rules complex and difficult to manage. This can lead to maintenance challenges and potential inefficiencies.(Palensky and Dietrich, 2011b) 2.4. Optimization-Based Algorithms Optimization-based algorithms are essential in the design of energy management systems (EMS), where they aim to ensure efficient, cost-effective, and sustainable use of energy resources. These algorithms formulate decision-making problems as mathematical optimization models that consider objectives (e.g., cost minimization) subject to operational constraints. 2.5. They are used across domains such as • Smart grids • Microgrids • Building energy systems • Electric vehicle fleets • Industrial energy systems • Description These methods mathematically model energy consumption as an optimization problem, often with constraints such as comfort levels, budget, or appliance operation limits. 2.6. Popular Techniques • Linear Programming (LP) • Mixed-Integer Linear Programming (MILP) • Dynamic Programming (DP) • Genetic Algorithms (GA) • Particle Swarm Optimization (PSO) • General Optimization Model 2.7. A typical energy management optimization problem can be formulated as 𝑚𝑖𝑛 𝑥 𝐶(𝑥)= ∑[𝑃𝑡𝑔𝑟𝑖𝑑.𝜆𝑡+ ∑𝐶𝑖,𝑡 𝑔𝑒𝑛(𝑥) 𝑖] 𝑇 𝑡=1 2.7.1. Subject to Power Balance Constraint 𝑃𝑡𝑙𝑜𝑎𝑑 =𝑃𝑡𝑔𝑟𝑖𝑑+∑𝑃𝑖,𝑡 𝑔𝑒𝑛 𝑡(𝑥)+𝑃𝑡𝑠𝑡𝑜𝑟𝑎𝑔 Storage Constraints 𝐸𝑡+1 =𝐸𝑡+ 𝜂𝑐ℎ.𝑃𝑡𝑐ℎ−𝑃𝑡𝑑𝑖𝑠 𝜂𝑑𝑖𝑠 Global Journal of Engineering and Technology Advances, 2025, 23(03), 327-332 330 Operational Limits 𝑃𝑖𝑚𝑖𝑛 ≤ 𝑃𝑖,𝑡 𝑔𝑒𝑛 (𝑥) ≤𝑃𝑖𝑚𝑎𝑥 2.7.2. Were • 𝐶(𝑥) : Total Cost • 𝑃𝑡𝑔𝑟𝑖𝑑 : Power drawn from the main grid at time 𝑡 • 𝜆𝑡 : Electricity Price at time 𝑡 • 𝐶𝑖,𝑡 𝑔𝑒𝑛(𝑥): Cost of generation from source 𝑖 • 𝐸𝑡: Energy Stored in the battery in battery at time 𝑡 • 𝜂𝑐ℎ,𝜂𝑑𝑖𝑠: Charging/discharging efficiencies • 𝑃𝑡𝑐ℎ,𝑃𝑡𝑑𝑖𝑠: Charging and discharging Power 2.8. Smart Grid Energy Scheduling (MILP) • Goal: Minimize electricity costs by optimally dispatching energy from grid, renewables, and batteries. • Method: Mixed-Integer Linear Programming (MILP). • Example Equation: Uses the cost minimization model above with binary variables for generator status(Ahn et al., 2018). 2.9. Building HVAC Optimization (NLP) • Goal: Minimize HVAC energy consumption while maintaining indoor comfort. • Method: Nonlinear Programming (NLP) due to temperature dynamics. • Example Addition: Incorporate thermal dynamics in constraints: (Old-world et al., 2012) 𝑇𝑖𝑛,𝑡+1 =𝑇𝑖𝑛,𝑡 +𝛼(𝑇𝑜𝑢𝑡,𝑡+ 𝑇𝑖𝑛,𝑡)+𝛽𝑃𝑡𝐻𝑉𝐴𝐶 2.10. EV Fleet Charging Optimization (DP) • Goal: Schedule EV charging to reduce peak demand and energy cost. • Method: Dynamic Programming (DP) across time horizon. 2.10.1. Example Cost Function 𝑚𝑖𝑛∑[𝜆𝑡 .𝑃𝑡𝐸𝑉] 𝑡 2.11. Microgrid Energy Management (GA) • Goal: Coordinate DERs and storage to minimize fuel cost and emissions. • Method: Genetic Algorithm (GA). • Fitness Function: Combines fuel cost and CO₂ emissions: 𝑓𝑖𝑡𝑛𝑒𝑠𝑠=𝛼.𝐶(𝑥)+𝛽.𝐸𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠(𝑥) 2.12. Advantages • Can handle complex constraints and objectives • Provides optimal or near-optimal solutions 2.13. Limitations • High computational requirements • May not perform well in highly dynamic environments Global Journal of Engineering and Technology Advances, 2025, 23(03), 327-332 331 3. AI and Machine Learning-Based Algorithms 3.1. Description 3.1.1. These algorithms use data-driven approaches to predict, classify, and control energy usage. Key Techniques • Neural Networks (e.g., Deep Learning for load prediction) • Reinforcement Learning (e.g., Q-Learning, DDPG) • Support Vector Machines (SVM) • Fuzzy Logic Systems Applications • Load forecasting • Appliance classification • Real-time energy control Advantages • Adaptive and capable of learning from data • Suitable for dynamic and non-linear environments Limitations • Requires large datasets for training • Interpretability and reliability can be challenging 3.2. Hybrid Approaches 3.2.1. Description These methods combine different algorithmic paradigms to leverage their respective strengths. 3.2.2. Examples • Fuzzy-GA: Combines fuzzy logic’s reasoning with GA’s optimization • Deep RL with MILP constraints • Rule-based reinforcement learning 3.2.3. Advantages • More robust and flexible • Can address multiple objectives (cost, comfort, emissions) 3.2.4. Limitations • Increased complexity in design and implementation • Trade-off in transparency vs performance Global Journal of Engineering and Technology Advances, 2025, 23(03), 327-332 332 4. Comparative Analysis Table 2 Comparative Analysis Algorithm Type Adaptability Computational Cost Scalability Optimality Rule-Based Low Very Low Low Low Optimization-Based Medium High Medium High AI/ML-Based High Medium to High High Medium to High Hybrid High High High High 5. Conclusion The field of home energy optimization has seen rapid advancements, driven by both conventional optimization techniques and emerging AI-driven models. Each class of algorithm offers unique benefits and trade-offs. While no single approach is universally best, hybrid and adaptive algorithms appear promising for future smart home energy management systems. Continued research is needed to improve their efficiency, robustness, and integration with evolving smart grid technologies. 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