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Uddhav Gursal,
S. K. Agrawal,
- Student, Department of Electrical Engineering, Sanjivani College of Engineering, Kopargaon, Maharashtra, India
- Assistant Professor, Department of Electrical Engineering, Sanjivani College of Engineering Kopargaon, Maharashtra, India
Abstract
Energy management in smart homes is becoming one of the main requirement because of fast growth in electricity demand, renewable sources, and electric vehicles. A proper household energy management system not only reduce the energy cost but also help to maintain stability of power grid. Demand Response (DR) program play very big role in this process by shifting or controlling the load according to price and availability of renewable power. In this paper, an Optimized Household Energy Management System is presented by using DFDGCN-BWWPO technique. The Deep Feature Dynamic Graph Convolution Network (DFDGCN) is used to learn the complex relation between house appliances, user behavior and energy demand in time series manner. After that, the Binary Weighted Whale Particle Optimization (BWWPO) is applied to schedule the appliances in efficient way. By combining both methods, the system can handle uncertain renewable generation, reduce peak load, and improve demand response participation. and user comfort as crucial scheduling goals. Variations in household consumption and oscillations in renewable energy are also considered when the system is operating. The methodology is intended for simulation-based assessment under various demand response situations, household scenarios, and levels of renewable energy penetration. Electricity cost, peak-to-average ratio, prediction error, computing time, and comfort violations can all be used to evaluate its performance. The suggested method seeks to enhance family energy consumption, lower peak demand, and facilitate effective involvement in demand response initiatives by fusing graph-based learning with bio- inspired optimization. Additionally, the framework can be integrated with electric vehicle charging systems and domestic energy storage.
Keywords: Household Energy Management System, Demand Response, Deep Feature Dynamic Graph Convolution Network (DFDGCN), Binary Weighted Whale Particle Optimization (BWWPO), Renewable Energy Integration.
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Trends in Electrical Engineering
| Volume | 16 | |
| 03 | ||
| Received | 30/07/2026 | |
| Accepted | 13/10/2026 | |
| Published | 30/10/2026 | |
| Publication Time | 92 Days |