Load Forecasting Based on Machine Learning Techniques

Load forecasting is a critical phenomenon in the planning and operation of electrical power systems, enabling utility providers to ensure reliable and cost-effective energy distribution. This main objective of work is to develop a predictive model for short-term load forecasting using advanced machine learning techniques. The methodology involves the implementation of the ARIMA (Auto Regressive Integrated Moving Average) model as a conventional statistical approach for time series forecasting. In parallel, machine learning models such as Linear Regression, Random Forest and XG Boost are employed to learn complex patterns and nonlinear relationships within the dataset.