Optimizing Appliance Scheduling for Solar Energy Management Using Metaheuristic Algorithms

2026-06-12 · Source: cs.AI updates on arXiv.org · Field: Energy & Utilities — Renewable Energy Systems, Artificial Intelligence & Machine Learning, Energy Efficiency & Conservation · Depth: Expert, extended

Summary

Hiba Ahmed et al. (2026) present a metaheuristic-based framework for optimizing residential appliance scheduling in solar-powered smart homes. This approach addresses the mismatch between intermittent solar energy generation and household consumption by determining optimal appliance start times. The framework utilizes Iterated Local Search (ILS) and Simulated Annealing (SA) to maximize renewable energy utilization while minimizing user inconvenience. It uniquely extends scheduling beyond a single day to accommodate "spillover" tasks, ensuring operational continuity across multiple days. The model incorporates constraints such as appliance operating durations, power consumption, a 7.5 kW inverter limit, battery state of charge, and 48-hour solar generation forecasts. Experimental results from an 86-day simulation demonstrate that SA consistently outperforms ILS, achieving lower user dissatisfaction and more stable performance. The study also emphasizes that proper inverter sizing is critical for feasible scheduling and high user satisfaction.

Key takeaway

For AI Scientists and Research Scientists developing smart home energy management systems, this research indicates that Simulated Annealing (SA) is a superior choice for optimizing appliance schedules. You should prioritize SA over Iterated Local Search (ILS) for its consistent performance and lower user dissatisfaction in multi-day solar energy systems. Ensure your models incorporate multi-day task spillover and carefully consider inverter sizing, as these factors are critical for achieving feasible and user-satisfying schedules.

Key insights

Metaheuristic algorithms can optimize multi-day appliance scheduling in solar homes, balancing energy use and user comfort.

Principles

• Multi-day scheduling improves continuity for long tasks.
• Simulated Annealing (SA) offers stable, superior performance.
• Inverter capacity directly impacts scheduling feasibility.

Method

A rolling-window framework uses 48-hour PV forecasts and metaheuristics (ILS/SA) to optimize daily appliance start times, accounting for battery SoC and cross-day spillover.

In practice

• Implement SA for robust appliance scheduling.
• Design systems with multi-day task spillover.
• Size inverters appropriately for solar homes.

Topics

• Appliance Scheduling
• Solar Energy Management
• Metaheuristic Algorithms
• Simulated Annealing
• Iterated Local Search
• Smart Home Energy Systems
• Battery State of Charge

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.