Machine learning applied to electric load forecast: A study comparing results obtained from various regression methods

Short-term electricity load forecasting is pivotal in efficient energy management and resource allocation within power systems. In this research paper, we present a comparative analysis of regression methods for short-term electricity load forecasting, implemented in Python using machine learning techniques. The study explores the performance of various regression models including linear regression, support vector regression, decision tree regression, and random forest regression. Historical load data from a real-world electricity grid is utilized for training and evaluating the forecasting models. The evaluation metrics considered include Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) to assess the accuracy and robustness of the models. Through extensive experimentation and analysis, insights into the strengths and weaknesses of each regression method in the context of short-term electricity load forecasting are provided. The findings of this study contribute to the advancement of predictive modeling techniques in the domain of energy forecasting and facilitate informed decision-making for energy stakeholders.