Abstract
In response to climate change and continued reliance on traditional high-carbon fossil fuels, promoting the transition towards sustainable energy systems by development of low-carbon energy resources has been seen as the main strategy for mitigating and solving global climate change. However, the promotion of low-carbon energy also faces material supply risks. To provide a reference for the steady and rapid development of renewable energy and other energy in the future energy market, this paper considers renewable energy prediction based on an LSTM model as well as the growth rate changes of crude oil, natural gas, nuclear energy, financial revenues and expenditure. In the prediction process, it is found that natural gas will be a strong competitor for the development of renewable energy in the future. When natural gas grows too quickly, the growth of renewable energy will be negative. On the other hand, when the monthly growth rate of natural gas and crude oil is smaller than that of nuclear energy, renewable energy will display a growth trend, and the rate will increase with the growth of natural gas and nuclear energy. What’s more, wind and solar energy will be limited by metallic materials, such as Dy, Nd, Te, In, etc. Improving the energy density of metals plays a key role in China's transition to a low-carbon energy structure.
Highlights
Renewable energy is an important means to control and reduce carbon emissions in the process of heat generation and transportation
The first kind of prediction research mentioned earlier is relatively mature; in this kind of study, the most widely used prediction method is the use of regression model (Wu et al, 2019) as well as fuzzy logic-based prediction (Sivaneasan et al, 2017), autoregressive integrated moving average model (ARIMA) (Aasim et al, 2019), Gray model (GM) (Liu et al, 2016), support vector regression (SVR) (Almusaylh et al, 2018), and artificial neural network (ANN) (Ghimire et al, 2019)
It has been shown in section “Model Building and Scenario Setting” that the prediction of renewable energy by the longand short-term memory network (LSTM) model requires more accurate input of the five data indexes: crude oil, natural gas, nuclear energy, and fiscal revenue and expenditure
Summary
Renewable energy is an important means to control and reduce carbon emissions in the process of heat generation and transportation. Its main output form is power supply, which can be directly put into production and application. It does not need a large amount of investment in transportation infrastructure like traditional energy required in the past. China’s overall electricity demand has been growing rapidly, according to the International Energy Agency (IEA). The renewable energy generation capacity increased from 126.788 to 1570.566 TWh, among which wind power generation capacity increased from 0.002 to 237.071 TWh and solar photovoltaic power generation capacity increased from 0.002 to 75.256 TWh. The data show that China’s electricity consumption and demand for renewable energy have been growing steadily
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