Load Forecasting Using Random Forest Regression Algorithm in Machine Learning

Load forecasting, a crucial aspect of energy management, involves predicting the future electricity demand based on historical data. In the context of monthly time series analysis, this study focuses on applying machine learning techniques, particularly Random Forest Regression, for load forecasting. The study's dataset, "kaggle," is carefully preprocessed to ensure suitability for analysis. Preprocessing involves aggregating the data weekly and transforming the 'date' column into a datetime object. As part of the exploratory data analysis (EDA) procedure, autocorrelation plots are utilized to gain insights into the underlying connections within the data. The monthly load data reveals temporal patterns and dependencies, which are explored through visualization tools to enhance comprehension and inform further modeling stages. Using the preprocessed dataset, the primary goal is to train a Random Forest Regression model. The data is divided into training and testing sets to facilitate model training and evaluation. Load estimates for upcoming times are generated using the trained model. Evaluation of the forecasting model's performance employs measures such as Mean Squared Error (MSE) and R-squared (R2) Score. These criteria quantify the precision and reliability of machine learning-based load projections. Overall, the study demonstrates the effectiveness of machine learning methods, specifically Random Forest Regression, in predicting energy loads. The practical utility of these algorithms in optimizing resource allocation and enhancing power system efficiency is emphasized through real-world examples in energy management.