Town gas daily load forecasting based on machine learning combinatorial algorithms: A case study in North China

Timely and accurate gas load forecasting is critical for optimal scheduling under tight winter gas supply conditions. Under the background of the implementation of “coal-to-gas” for winter heating in rural areas of North China and the sufficient field research, this paper proposes a correction algorithm for daily average temperature based on the cumulative effect of temperature and a set of combined forecasting models for gas load forecasting based on machine learning and introduces its application through a detailed case study. In order to solve the problems of forecasting performance degradation and complexity increase caused by too many influencing factors, a combined forecasting model back-propagation-improved complete ensemble empirical mode decomposition with adaptive-noise-gated recurrent unit based on residual sequence analysis is proposed. Back propagation(BP) neural network is used to analyze the main influencing factors, so that the secondary influencing factors are reflected in the residual sequence generated by the forecasting. After decomposition, reconstruction, and re-forecast, the mean absolute percentage error(MAPE) of the combined models for the daily gas load in the case study has been controlled under 1.9%, which is significantly improved compared with each single algorithm.The forecasting error before and after the temperature correction are also compared. It is found that the MAPE with the temperature correction is reduced by 1.7%, which reflects the effectiveness of the temperature correction to eliminate the impact of temperature cumulative effect and its contribution to the improvement of the forecasting accuracy for the combined forecasting models.