Forecasting Energy Consumption Based on SVR and Markov Model: A Case Study of China - Meng, Zhaosu; Sun, Huike; Wang, Xi (Frontiers in Environmental Science, 2022)
Forecasting energy demand in emerging nations is a critical policy tool utilized by decision makers worldwide. However, as estimated economic and demographic characteristics frequently diverge from realizations, precise forecast results are difficult to get due to the economic system's intrinsic complexity. This work proposed a machine learning model for estimating energy consumption in China using the support vector regression model (SVR). Additionally, Markov Chains (MC) is employed to forecast and analyze the evolving energy consumption structure. The results demonstrate that SVR model is more accurate (98.4 %) than the linear model (Moving Average model), the nonlinear model (Grey model), and past research in predicting energy usage. Under the current rate of energy consumption, China's total energy consumption will break through six billion in the next four years. Furthermore, it is expected that China's energy consumption structure will be more rational in 2025, with increased non-fossil energy consumption and decreased coal consumption, while natural gas consumption continues to grow at a low rate. It provides scientific basis for the implementation of carbon emission peak action, energy security and energy development plan during the 14th Five-Year Plan period.