Application of Forecasting Models in Electrical Engineering: A Systematic Literature Review

Electricity demand forecasting stands as a paramount factor in the operation and strategic planning of energy production and distribution systems. This article aims to present the application of forecasting models in electrical engineering area through a systematic literature review of 50 papers (45 journal articles and 5 conference papers) published between 2016 and 2023. In fact, electric demand forecasting plays a crucial role in decision making in the realization of the next-generation power system concept, such as robust sizing of the power system and efficient energy management. Nevertheless, the existence of various forecasting models poses a challenge for researchers, making it difficult to select the best model. Therefore, a main objective of this work is to identify new potential applications for addressing challenges in the selection of forecasting electricity models. To conduct our analysis, we employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology as a formal guideline for data collection. The results reveal that Artificial Intelligence models are frequently utilized for long-term electricity forecasting, comprising 42% of the cases studied. Moreover, the review papers typically used key performance indicators to compare and select the best forecasting model. However, researchers are confronted with the task of selecting the most appropriate model. Based on the findings from our review, we highlight novel research direction the sustainability aspect within the domain of forecasting electricity demand.