Enhance Home Energy Management System Using Machine Learning

Abstract

This paper presents a new home energy management system (ML-based HEMS) based on Machine Learning that will help to support the most efficient utilization of energy in the household, decrease their spending, and improve the integration of renewable energy resources. As compared to conventional rule-based and optimization-based systems, which do not have the experience of dealing with dynamic conditions, the proposed one utilizes machine learning techniques such as supervised learning, neural networks and reinforcement learning to come up with real-time and data-driven decisions. With the integration of predictive models and adaptive algorithms, the system can be dynamically controlled in terms of appliance scheduling according to energy demand forecasting, energy costs, and renewable energy supply, leading to controlling energy use accurately. Quantitative results of the study prove that the ML-based HEMS was able to reduce the energy costs by 22 percent in comparison with traditional systems, which can be said to be effective in lowering the household electricity costs. Moreover, the system showed a peak load reduction of 18 percent, thus relieving the grid stress and increasing the stability of energy. These findings emphasize the potential of machine learning to transform home energy management not only as a means of reducing expenses, but also as a means of making energy systems more sustainable by more efficiently utilizing renewable energy such as solar power. The research leads to more versatile and intelligent house electrical systems that can learn and evolve based on the user and the situation they are in. The proposed ML-based HEMS will be a significant leap towards the latent of integrating smart home technologies and energy grids as a scalable option that will lead to greater energy efficiency, reduce the cost of operations, and bring about a more energy-friendly future. The future may further improve scalability and the ability to make real-time decisions regarding more complex environments.