US natural gas consumption prediction using an improved kernel-based nonlinear extension of the Arps decline model

Natural gas (NG) is a vital energy in the energy structure transition, and its consumption prediction is a significant issue in energy structure management and energy security. As the second largest energy consumer and producer in the world, the status of NG in the United States (US) energy system has been increasing since the An America First Energy Plan was proposed in 2017. Accurate prediction of natural gas consumption (NGC) can provide an effective reference for decision-makers, policymakers, and energy companies. This paper proposes an improved kernel-based nonlinear extension of the Arps decline model (KNEA) to forecast NGC in the US. The grey wolf optimization (GWO) algorithm is used to optimize the regularization parameter and kernel width in the KNEA model, and applies the hybrid model to the NGC datasets of different sectors (including lease and plant fuel usage, pipeline and distribution usage, residential users, commercial users, industrial users, vehicle fuels users, and power generation users) in the US. Compared with the prediction results of five benchmark models, it is shown that the GWO-KNEA model has the best performance in each dataset, and the range of mean absolute percentage error is less than 5%. By comparing the computational time and memory occupancy of the model, it can be concluded that the time and space complexity of the GWO-KNEA model is greater than that of the original KNEA model, but lower than that of other benchmark models. Moreover, this paper uses the newly proposed model to predict the NGC and consumption mix of the US from 2019 to 2025. The main conclusions are drawn: (1) NGC in the US will show a slow growth trend (the average annual growth rate is only 1.2%); (2) The proportion of NGC in power generation will increase significantly, reaching about 39% in 2025; (3) The proportion of residential, commercial and industrial NGC will decline slightly. (C) 2020 Elsevier Ltd. All rights reserved.