A state-of-the-art comparative review of load forecasting methods: Characteristics, perspectives, and applications

Abstract

The rapid growth in electricity demand, driven by its expanding applications across diverse sectors, has emphasized the criticality of maintaining a balanced and reliable power supply. Accurate load forecasting has become a cornerstone of modern power system management, enabling the efficient planning, operation, and design of electrical grids. With the increasing penetration of renewable energy sources and the rise of smart grid technologies, the need for precise forecasting methodologies has intensified to ensure enhanced grid stability, efficiency, and seamless renewable integration. This article systematically reviews contemporary state-of-the-art forecasting techniques, critically analyzing their performance, applications, and outcomes. Emphasis is placed on methodologies for predicting renewable energy availability, electricity pricing, and load demand, with an in-depth evaluation of their modeling frameworks and predictive accuracies. The review highlights significant advancements in artificial intelligence-based approaches, particularly machine learning and neural network models, which consistently outperform traditional forecasting methods in terms of precision and robustness. For enhanced clarity, key insights and comparative analyses are summarized in comprehensive tables, facilitating efficient reference. This review aims to provide researchers with a thorough understanding of advanced forecasting models, their capabilities, and limitations, thereby guiding future research endeavors in the domain of load forecasting.

Keywords

• Load forecasting
• Artificial intelligence in power systems
• Renewable energy integration
• Smart grid stability
• Machine learning for energy demand prediction
• Operation and planning