Optimizing Appliance Scheduling for Solar Energy Management Us-ing Metaheuristic Algorithms

Citation

Ahmed H, Brownlee AEI, Adair J & Powers ST (2026) Optimizing Appliance Scheduling for Solar Energy Management Us-ing Metaheuristic Algorithms. In: GECCO '26 Companion: Genetic and Evolutionary Computation Conference Companion. Genetic and Evolutionary Computation Conference, San José, Costa Rica, 13.07.2026-17.07.2026. https://doi.org/10.1145/3795101.3805310

Abstract
Solar energy generation is often misaligned with when households use power, creating a scheduling challenge. We optimize appliance start times in an island microgrid setting to minimize user dissatisfaction while promoting solar use and respecting system constraints. A sequential multi-day scheduling framework using Iterated Local Search (ILS) and Simulated Annealing (SA) considers power consumption, active duration, inverter size, battery limits, and solar forecasts, opening potential to explore trade-offs between cost, system size, and satisfaction.

Keywords
Optimization; Scheduling; Metaheuristics; Renewable Energy; User Satisfaction

Journal
Genetic and Evolutionary Computation Conference (GECCO Companion '26)

• Optimizing Appliance Scheduling for Solar Energy Management Using Metaheuristic Algorithms