[China's Energy Consumption and Carbon Peak Path Under Different Scenarios].

ABSTRACT

Accurately predicting energy consumption and carbon emission is important for China to make energy and carbon emission policy formulation more scientific and to achieve the goal of carbon peak before 2030 and carbon neutrality before 2060. Since energy demand is affected by numerous complex factors, it is hard to capture the dynamically developing rules of energy consumption comprehensively. Therefore, a novel two-layer decomposition-ensemble forecasting approach that was optimized by an improved particle swarm optimization algorithm based on simulation anneal and position disturbance strategy (IPSO) was proposed. Firstly, trend decomposition (TD) was utilized to break energy consumption time series down into a trend and a non-trend subseries. Then, empirical mode decomposition (EMD) was adopted to break the non-trend subseries down into several intrinsic mode functions (IMFs) and a residuum subseries. Subsequently, the aforementioned trend subseries, intrinsic mode functions, and residuum series were respectively modeled for prediction. The trend subseries was predicted using the multivariate linear regression model (MLR), which was optimized using IPSO. Both IMFs and residuum series were predicted using long short-term memory (LSTM). Finally, the final prediction of energy consumption was obtained by integrating the forecasting results of these subseries. According to China's energy consumption empirical analysis, the proposed IPSO-MLR-LSTM forecasting model based on the two-layer decomposition-ensemble approach using TD-EMD combined the advantages of TD, EMD, IPSO, and LSTM, which could comprehensively extract the developing rules of energy consumption by implementing a deeper decomposition strategy. Therefore, it is feasible and effective to apply the proposed forecasting model for energy consumption prediction. Finally, the energy consumption and carbon emissions of China under different energy consumption structure, economic growth, population, energy efficiency, and household energy consumption per capita scenarios in 2021-2035 were predicted. Then, some relevant policies and suggestions were put forward based on the forecasting results.