A novel grey seasonal model based on cycle accumulation generation for forecasting energy consumption in China

For time series forecasting, the mobile holiday effect typically brings certain difficulty in obtaining accurate forecasts for monthly and quarterly series since it can result in enormous disturbances for modeling, especially for the sequences with limited information and much uncertainty. This study proposes a discrete grey seasonal model by cycle accumulation generation as an alternative approach to seasonal time series forecasting to effectively address such issues. Moreover, the logarithmic transformation technique is introduced to enhance the forecasting performance of this proposed approach, which can significantly reduce the errors resulted from the cycle accumulation generation restoration. To validate the effectiveness and practicality of the proposed model, a range of competing models, including the DGSM(1,1), SFGM(1,1), SGM(1,1), SARIMA, BP neural network, LSSVM, RBF, and LSTM models, are employed to conduct experimental comparisons. Empirical evidence from the monthly industrial electricity and quarterly natural gas consumptions show that the newly designed approach is superior to other competitors in terms of level accuracy and performance stability with the support of the logarithmic transformation technique. Further, the robustness test over the performance of the logarithmic transformation and cycle accumulation generation techniques has been implemented. Results demonstrate that the proposed approach with the logarithmic transformation technique's support can be considered a promising tool to weaken the mobile holiday' disturbance and improve the forecasting accuracy. Therefore, based on the accurate predictions, the power supply department is able to prepare the power supply plans in advance, reducing the negative effects of the holiday effect on energy management.