• Deepkant Ujjaini
• Raghunandan Singh Baghel

Renewable energies are being introduced in countries around the world to move away from the environmental impacts from fossil fuels. In the residential sector, smart buildings that utilize smart appliances, integrate information and communication technology and utilize a renewable energy source for in-house power generation are becoming popular. Accordingly, there is a need to understand what factors influence the accuracy of managing such smart buildings. Thus, this study reviews the application of machine learning prediction algorithms in Energy Management Systems. Various aspects are covered, such as load forecasting, household consumption prediction, rooftop solar energy generation, and price prediction. Also, a proposed Home Energy Management System framework is included based on the most accurate machine learning prediction algorithms of previous studies. This review supports research into the selection of an appropriate model for predicting energy consumption of smart buildings.

Home Energy Management System, Machine Learning algorithm, Prediction, Forecasting, Optimization

"ENHANCEMENT OF POWER SYSTEM DEMAND SIDE MANAGEMENT AND FORECASTING OF GRID PERFORMANCE USING MACHINE LEARNING APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.c684-c690, May-2025, Available :http://www.jetir.org/papers/JETIR2505290.pdf

"ENHANCEMENT OF POWER SYSTEM DEMAND SIDE MANAGEMENT AND FORECASTING OF GRID PERFORMANCE USING MACHINE LEARNING APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppc684-c690, May-2025, Available at : http://www.jetir.org/papers/JETIR2505290.pdf