Optimizing Electricity Costs in Home Energy Management Systems Using Hybrid Computational Techniques for Sustainable Energy Efficiency

He said that home energy management systems (HEMS) are essential for man aging energy use and Bills in homes. New improvements in the cross-disciplinary area which comprises the use of machine learning, optimization algorithms, as well as real-time data analysis can be viewed as future opportunities to improve the utilization of HEMS. The present paper is a systematic examination of low-power techniques for reducing electricity costs without compromising the comfort of the user or the power usage. The proposed system is designed to in clude predictive analytics and multi-objective for purposes of optimization of costs and energy usage. Simulation results also affirm that the hybrid approach is more effective than the conventional approach. The major contributions are the incorporation of dynamic pricing models and adaptive control systems, which combine to yield significant cost optimization. In this paper, how the proposed hybrid HEMS model was designed, implemented, and then assessed in terms of its performance to determine whether it can support sustainable energy manage ment needs will be highlighted.