Electricity demand forecasting methodologies and applications: a review - Prosper O. Ugbehe; Ogheneruona E. Diemuodeke; Daniel O. Aikhuele (Sustainable Energy Research)
Abstract 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.