Forecasting the daily natural gas consumption with an accurate white-box model.

Compared with artificial intelligence black-box models, statistical white-box models have less application and lower accuracy in forecasting daily natural gas consumption that contains high dimensional and large samples. Parallel model architecture (PMA) is a forecasting strategy that improves the accuracy of forecasting models. However, due to the large numbers of non-stationarity subseries generated by PMA in daily natural gas consumption forecasting, the forecasting problem becomes more difficult. This paper proposes a weighted parallel model architecture (WPMA) strategy that reduces the numbers and the non-stationarity of subseries by introducing k-means clustering and weighting the forecasts of subseries for out-of-sample forecasting. By combining WPMA with principal component analysis (PCA) and multiple linear regression (MLR), a white-box hybrid model is generated called PCA-WPMA-MLR. Principal component analysis is a dimension-reduction algorithm that is used to extract the components from input variables, and MLR is a white-box forecaster. Additionally, the historical datasets of four representative cities distributed in three climate zones are collected in case studies. The results show that the PCA-WPMA-MLR model provides comparable forecasting performance with the deep learning model. WPMA outperforms PMA in improving forecasting accuracy, and it reduces the mean absolute percentage error of MLR by 39.07% in the Melbourne case. • A forecasting strategy, namely weighted parallel model architecture, is proposed. • A novel white-box hybrid model is generated. • The novel model provides comparable forecasting performance with LSTM. [ABSTRACT FROM AUTHOR]