Integrating Artificial Intelligence for Optimized Load Forecasting in Electrical Power Systems

Abstract

Load forecasting plays a pivotal role in the operational planning and management of modern electrical power systems. With the increasing integration of distributed energy resources (DERs), electric vehicles, and renewable energy sources, traditional forecasting methods have struggled to maintain accuracy. This paper presents an AI-based approach, specifically using Long Short-Term Memory (LSTM) neural networks, to predict short-term and long-term electrical load demands. We collected real-time data from multiple substations and processed it using machine learning models that consider weather data, historical loads, and peak demand intervals. Comparative analysis with ARIMA and SVM models demonstrated a significant reduction in forecasting errors. The study highlights the advantages of AI in improving decision-making for grid operators and ensuring economic and reliable power supply.

Keywords: Load Forecasting, Artificial Intelligence, LSTM Networks, Grid Management, Power System Optimization.