INTELLIGENT LOAD FORECASTING USING MACHINE LEARNING ALGORITHMS

Abstract

Accurate load forecasting plays a vital role in the efficient operation and planning of modern power systems. With the increasing penetration of renewable energy sources and smart grid technologies, traditional forecasting methods are often insufficient to handle complex and nonlinear load patterns. This paper presents an intelligent load forecasting approach using machine learning algorithms to improve prediction accuracy and reliability. The proposed framework integrates data preprocessing, feature selection, and advanced learning models to capture temporal and seasonal variations in electricity demand. Multiple machine learning techniques are evaluated for short-term and medium-term forecasting. Experimental results demonstrate significant improvement over conventional forecasting models. The approach enhances grid stability and operational efficiency. The study highlights the effectiveness of intelligent forecasting for modern energy systems.