Genetic-Based Optimized Energy Management at Smart Home Considering the Increase Consumer Convenience and Priority

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Ahmed Chfat Abd Zaid, Behrouz Tousi


In recent years, as household energy use has risen, there has been a greater emphasis on intelligent energy management within smart homes. The energy crisis is now one of the world's most pressing issues. With fossil fuel limitations and escalating electricity costs due to increased demand, there is a pressing need for research and effective solutions in this field. Building smart homes is one approach to reducing energy usage. In these homes, consumers can not only control their energy consumption but also sell excess energy back to the grid using renewable sources like solar and wind. This dissertation aims to create a smart home energy management system (HEMS) to efficiently operate residential electrical appliances. The model prioritizes optimizing energy use to enhance user comfort, thermal comfort levels, and profitability of smart home energy management, aligning with consumer preferences. The results demonstrate the model's effectiveness. It uses consumption data and current U.S. electricity market prices, running simulations on an Intel Core i5-6200U processor system with 64-bit Windows 10, Matlab R2017b, and Genetic Algorithm (GA) optimization.

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