Conventional models and artificial intelligence-based models for energy consumption forecasting: A review

Abstract Conventional models and artificial intelligence (AI)-based models have been widely applied for energy consumption forecasting over the past decades. This paper reviews conventional models and AI-based models in energy consumption forecasting, and discusses the models in the aspects of forecasting horizon, applied areas, type of model, and forecasting accuracy. The conventional models are categorized into time series models, regression models, and gray models. The AI-based models are grouped into artificial neural network-based models and support vector regression machine-based models. Additionally, to the best of our knowledge, the evaluation of the models' performance in different forecasting horizons is a critical issue that has not been solved. Thus, for better evaluate the performance of forecasting models, a detailed reference range of mean absolute percentage error (MAPE) in energy consumption forecasting will also been proposed. The review results show that conventional models are preferred for the yearly energy consumption forecasting in national level. Among them, nonlinear regression models can not only explicitly describe the relationship between consumption data and influencing factors but also obtain the lowest average MAPE (1.79%) for long-term energy consumption forecasting. AI-based models are robust and full-scale in all applied areas and forecasting horizons. This paper provides valuable suggestions for researchers in model selection and serves as an initial study of the evaluation benchmark construction for energy consumption forecasting.