Electricity plays a crucial role in driving economic growth and ensuring the stability of a country. While electricity demand and supply tend to remain stable in developed nations, many developing countries continue to face persistent electricity crises. The primary objective of this meta-analysis is to identify gaps in the literature related to electricity demand forecasting methodologies and to examine the recommendations proposed by researchers in the context of three selected countries. This study presents a comprehensive overview of statistical and machine learning models used in electricity demand forecasting, based on a review of 107 articles published between 2005 and 2023, highlighting the common variables, influencing factors, and forecasting horizons considered. The literature reveals that research motivations differ according to the resources and contextual factors of each country: in developing countries, studies are often driven by economic indicators, as well as social and household parameters, and predominantly employ traditional prediction methods; in contrast, studies in developed countries incorporate climate variables alongside other indicators and tend to adopt modern modelling techniques. Findings reveal that developing countries predominantly rely on traditional statistical methods driven by economic, social, and household indicators, whereas developed countries integrate climate variables and increasingly adopt advanced modelling techniques. The study identifies methodological gaps and provides insights to enhance the accuracy and applicability of future electricity demand forecasting models.
omar et al. (Sun,) studied this question.