Home Energy Management

Home energy management (HEM) systems aim to optimize residential energy consumption, reducing costs and carbon footprint through intelligent control of appliances and integration of renewable energy sources. Current research emphasizes accurate short-term energy consumption forecasting using machine learning models like convolutional neural networks, recurrent units, and support vector regression, often enhanced by techniques such as transfer learning and association mining to improve prediction accuracy even with limited data. These advancements, validated through real-world deployments, demonstrate significant potential for reducing energy waste and improving grid stability, impacting both scientific understanding of energy consumption patterns and the development of more sustainable homes.