Smart Energy Load Prediction using Machine Learning Techniques

Abstract

Sustainable electrical network expansion, smart grid management, and effective power production all depend on accurate energy demand forecasts. Because machine learning (ML) approaches can simulate complicated nonlinear electrical load patterns and analyze historical data, they are frequently employed. In order to forecast energy consumption and assist with power production planning, this research used machine learning techniques such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Multiple Linear Regression (MLR), and Adaptive Neuro-Fuzzy Inference System (ANFIS). Real historical power use data was used for both short-term and long-term forecasting assessments. According to the findings, SVM and ANN outperformed other techniques in terms of predicting accuracy and estimating mistakes. These methods enhance the accuracy of energy demand forecasting and facilitate efficient energy management in contemporary smart grid systems.