review · Sustainable Energy Research
Accurate estimation of future load requirements is essential for planning and operating electric power systems. A review of global literature categorises existing forecasting methodologies into statistical techniques, machine learning or artificial intelligence models, and hybrid approaches. Although machine learning and artificial intelligence methods are deployed more frequently than traditional statistical models, hybrid models represent the preferred option. By combining the strengths of multiple forecasting methods, hybrid approaches offer sustained accuracy, improved flexibility, higher precision, cost savings, and reduced volatility. The evaluation highlights current operational challenges alongside prospects for further research into electricity demand forecasting, focusing on power system management both globally and within Nigeria.
Electric power systems require precise demand predictions to ensure reliable operation and long-term infrastructure planning. Understanding which forecasting models perform best helps energy operators choose tools that minimise costly forecasting errors. By reducing volatility and improving estimation accuracy, modern forecasting methodologies support more stable, cost-effective energy grids for communities and industries.
Power system operators and energy planners can use hybrid forecasting frameworks to improve grid management and load planning. Because this work is a literature review rather than a newly deployed tool, the insights are at an early conceptual stage for users seeking guidance on selecting and integrating forecasting methods. Real-world adoption depends on implementing these combined models within specific utility operational environments.
AI-generated from the published abstract. Always read the original work before citing.
Electricity demand forecasting has emerged as a critical area of research in recent times, driven by the necessity for accurate predictions of future load requirements. Such predictions are essential for effectively operating and planning electric power systems. Various forecasting methodologies and approaches have been employed to estimate electricity demand, emphasizing the need for precision and informed analysis in electricity management. Accordingly, diverse approaches have been utilized within the research community to provide optimal estimates for future electricity demand. This study evaluates the global trends and advancements in electricity demand forecasting methodologies through a comprehensive review and analysis of existing literature relating to electricity demand management, electricity forecasting methodologies and applications. The forecasting methodologies are categorized into statistical, Machine Learning/Artificial Intelligence (ML/AI), and hybrid models. The findings indicate that while ML/AI-based models are applied more in electricity demand forecasting as compared with statistical models, hybrid models are preferred for their sustained accuracy, enhanced abilities in flexibility, productivity, talent pool, cost saving, precision, and reduced volatility. This emerging reliance on hybrid models is attributed to the integration of the forecasting capabilities of different models. The review finally recapped the challenges and opportunities for future research in electricity demand forecasting in Nigeria and globally.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1186/s40807-025-00149-z
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
