LAUSR

Abstract Natural gas consumption forecasting technology has been researched for 70 years. This paper reviews the history of natural gas consumption forecasting, and discusses the changes in forecasting horizons, influencing factors, and forecasting performance. According to the characteristics of forecasting models used in different periods, the history of natural gas consumption forecasting can be categorized into initial stage, conventional stage, AI stage, and all-round stage. The stage characteristics, typical models, advantages and disadvantages at different stages have been summarized. The review results show that, affected by the development of computer science and AI technology, short-term forecasting is the fastest-growing forecasting horizon, followed by long-term and medium-term. Additionally, long-term forecasting is mainly affected by production, population, and economic variables. Medium-term forecasting is mainly affected by economic and temperature variables. Influencing factors of short-term forecasting mainly depend on temperature variables, weather condition and date type. Furthermore, the statistical analysis of data characteristics, model characteristics and forecasting results presents that time series models are the best models for long-term forecasting. It has the lowest average mean absolute percentage error (1.90%) in long-term forecasting. To the medium-term and short-term forecasting, AI-based models present the best performance. Among them, artificial neural network models (2.21%) are preferred for medium-term forecasting, and support vector regression models (4.98%) are more suitable for short-term forecasting. Besides, this paper proposes a framework for model selection, and provides specific suggestions for future research directions.