Urban natural gas consumption forecasting by novel wavelet-kernelized grey system model

Natural gas is playing a key role in the Carbon Neutral path, which is clean and abundant. However it is difficult to collect sufficient data of urban natural gas consumption in China, and such data sets often present high nonlinearity and complex features, making it difficult to make accurate forecasts for the mid-small cities based on small samples. In this work, a novel wavelet kernel-based grey system model is proposed by using the wavelet kernel-based machine learning and the grey system modelling, taking advantage of the features of nonlinearity and periodicity of the wavelet kernel. A complete computational algorithm is presented by utilizing a hold-out cross validation-based grid-search scheme for selecting the optimal hyperparameters. Three case studies are carried out based on the real-world data sets of urban natural gas consumption in Kunming China, in which the proposed model outperforms other 15 time series forecasting models (including kernel-based models, grey system models and deep learning models), illustrating its priority in such forecasting tasks and high potential in similar applications.