A comparative analysis of modern load forecasting techniques

Abstract

Accurate load forecasting is a critical component in the efficient operation of modern energy systems, underpinning decisions in generation scheduling, grid management, and demand response. This study presents a comprehensive comparative analysis of contemporary forecasting methods for short- and medium-term load prediction. We review classical statistical models such as ARIMA and exponential smoothing, machine learning approaches including artificial neural networks, support vector machines, and decision trees, and hybrid techniques that integrate these paradigms via ensemble methods. Detailed methodological expositions and key mathematical formulations are provided, and the models are evaluated on synthetic data that mimic real-world energy load patterns. Our results reveal that, while the multi-layer perceptron demonstrates robust performance, the proposed ensemble approach does not consistently outperform its individual components, suggesting the need for further refinement in adaptive weighting strategies. The findings offer valuable insights into the advantages and limitations of each forecasting approach, thereby informing future research in adaptive ensemble modeling for energy load prediction.