Artificial Intelligence in Electricity Demand Forecasting

The article examines the field of electricity demand forecasting, with a particular focus on developing economies, such as India. It highlights the growing importance of dynamic yet accurate forecasting, considering the challenges and opportunities presented by the integration of Renewable Energy (RE), battery storage, electric vehicles, and the rise of Distributed Energy Resources (DERs). The research employs literature review to explore various forecasting methods, ranging from traditional statistical techniques, such as time series forecasting and econometric models, to innovative artificial intelligence and hybrid approaches. Through case reviews, end-user opinions, and expert interviews, it assesses these methods, emphasizing the need for ongoing innovation and adaptation in forecasting techniques. The findings suggest that more investment in forecasting resources is required, as further improvement a necessity. The evolving short-term forecasting methods complement the use of time series methods in medium-term forecasting and econometric techniques in long-term forecasting. In short-term forecasting, Artificial Intelligence (AI)-based methods, enabled by increased data availability, help overcome challenges such as weather variability and the growing use of distributed energy resources.