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Title	Comparative Study Of Genetic Algorithm And Particle Swarm Optimization For Load Scheduling And Cost Minimization In Energy Management Of Iot-Based Smart Homes
ID_Doc	15060
Authors	Shirsat G.; Mukherjee A.; Soni A.
Year	2025
Published	Lecture Notes in Electrical Engineering, 1276
DOI	http://dx.doi.org/10.1007/978-981-97-8464-6_9
Abstract	Electrical energy demand has expanded exponentially due to population expansion and urbanization. Smart cities are using IoT and smart gadgets in homes to solve this problem. Demand response (DR) programs involve these devices in the power market. This study provides a cost-effective energy management system for IoT-based smart homes that reduces electricity prices, optimizes energy use, and manages peak to Average ratio (PAR). Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are optimized for price-based DR programs in this research. The report recommends utilizing GA and PSO to schedule smart home loads and regulate IoT device energy usage. The strategy reduces consumer energy expenditures and improves user comfort (UC) in smart homes, encouraging sustainability and cost-efficiency. This approach could improve global energy management by encouraging smart house residents to live sustainably. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Author Keywords	Demand response programs; Energy management; GA; Internet-of-things; PAR; PSO; Smart devices; Smart Homes

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