Machine Learning-Based Load Forecasting Models for Power System Optimization

Abstract

Accurate load forecasting is critical for the efficient operation and optimization of modern power systems. Machine learning (ML) techniques have emerged as powerful tools to predict electrical load demand by capturing complex nonlinear relationships within historical data. This paper presents an overview and evaluation of various MLbased load forecasting models aimed at enhancing power system optimization. We explore models including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Gradient Boosting Machines (GBM), and Long Short-Term Memory (LSTM) networks, focusing on their forecasting accuracy, computational efficiency, and adaptability to changing load patterns. Using publicly available datasets, the models are trained and validated under diverse conditions, including short-term and medium-term forecasting horizons. The study incorporates key features such as weather variables, calendar effects, and socio-economic indicators to improve prediction accuracy. The comparative analysis highlights LSTM’s superior performance in capturing temporal dependencies, while GBM provides a balance between accuracy and interpretability. The paper also discusses the integration of these forecasting models into power system optimization frameworks, addressing challenges related to real-time data acquisition, model retraining, and scalability. Practical implementation considerations, such as handling missing data and model robustness against outliers, are evaluated. Results demonstrate that ML-based models significantly outperform traditional statistical methods in load forecasting accuracy, leading to enhanced grid reliability, better demand response strategies, and cost savings. Challenges such as data quality, model complexity, and computational requirements are identified, with recommendations to address them. This work underscores the importance of advanced ML techniques in power system load forecasting and provides guidance for practitioners seeking to implement optimized, data-driven energy management solutions.