![]() ![]() As a result, governments can use them to undertake energy-saving efforts. Using machine learning methods, energy consumption forecasts can be created with high accuracy. And better data interpretation and forecasting techniques are required.ĭue to their applicability, machine learning models are being utilized in a wide number of sectors, and their operation is similar to that of a function that best maps input data to output. To meet rising consumer demand and achieve green and sustainable growth, energy consumption structure should be optimized, with less coal consumption, and more clean energy. Conducting a thorough examination of future energy forecasts will not only aid in comprehending the future energy situation, but also in providing scientific support for overall energy planning and policy creation. While China’s energy conservation and emission reduction policies have had some initial success, the future situation remains unpredictable. As a result, forecasting energy use in China is crucial. Furthermore, in the General Debate of the 75th Session of the United Nations General Assembly in 2020, China has pledged to peaking carbon emissions by 2030 and reaching carbon neutrality by 2060, while also aiming to double the size of the Chinese economy by 2035. Long-term estimates are also required to determine the extent to which future trade and investment plans are required to secure China’s energy security. 1 As the biggest energy consumer of the world, China’s underlying demand and supply imbalances will have a significant impact on global energy markets. China’s total energy imports in 2020 is 1.20 billion tons, and this figure will rise further. Energy production has gradually increased from about 1.26 billion tons of ordinary coal to 4.08 billion tons, but it is still unable to satisfy energy demand, and the gap between the two is widening. With an economy anticipated to develop at a rate of 6–8% for decades, China’s influence in the global energy market is growing.īetween 19, China’s overall energy output and consumption increased steadily ( Figure 1). According to the National Bureau of Statistics, in 2021, China’s gross domestic product (GDP) was RMB 114.4 trillion ($17.7 trillion), up around RMB 13 trillion (United States $3 trillion) from 2020 1. This is especially true in rising energy markets like China. One of the most essential policy instruments utilized by decision makers worldwide is energy consumption predictions. 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. 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. Under the current rate of energy consumption, China’s total energy consumption will break through six billion in the next 4 years. 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. Additionally, Markov Chain (MC) is employed to forecast and analyze the evolving energy consumption structure. This work proposed a machine learning model for estimating energy consumption in China using the support vector regression model (SVR). 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. 2Institute of Marine Development of Ocean University of China, Qingdao, Chinaįorecasting energy demand in emerging nations is a critical policy tool utilized by decision makers worldwide.1School of Economics, Ocean University of China, Qingdao, China.Zhaosu Meng 1,2*, Huike Sun 1 and Xi Wang 1 ![]()
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