Optimum Load Forecasting Model-Based Intelligent Residential System Using Machine Learning Algorithms

Accurate load forecasting has become a challenge due to the unpredictable behavior of microgrid systems. As researchers and industries implement eco-friendly intelligent residential systems, there is a need to address energy consumption issues brought on by erratic human activity and weather. A self-sufficient and intelligent green residential network has been proposed to tackle this. This system uses machine learning to predict energy load by analyzing real-time data and considering renewable energy sources such as photovoltaic, wind power, and energy storage. The proposed energy management and load forecasting optimization model is based on machine learning algorithms. Specifically, the non-linear auto-regressive exogenous neural network has shown to be the most accurate at predicting future loads with an error rate of only 0.226%. Various machine learning algorithms were analyzed to determine the optimal load forecasting solution.