Abstract
In recent years, China’s terminal clean power replacement construction has experienced rapid development, and China’s installed photovoltaic and wind energy capacity has soared to become the highest in the world. Precise and effective prediction of the scale of terminal clean power replacement can not only help make reasonable adjustments to the proportion of clean power capacity, but also promote the reduction of carbon emissions and enhance environmental benefits. In order to predict the prospects of China’s terminal clean energy consumption, first of all, the main factors affecting the clean power of the terminal are screened by using the grey revelance theory. Then, an evolutionary game theory (EGT) optimized least squares support vector machine (LSSVM) machine intelligence algorithm and an adaptive differential evolution (ADE) algorithm are applied in the example analysis, and empirical analysis shows that this model has a strong generalization ability, and that the prediction result is better than other models. Finally, we use the EGT–ADE–LSSVM combined model to predict China’s terminal clean energy consumption from 2019 to 2030, which showed that the prospect of China’s terminal clean power consumption is close to forty thousand billion KWh.
Highlights
With the comprehensive deepening of China’s power industry, the “Thirteenth Five-Year Plan”outline has addressed the major challenges of industrial transformation and put forward a plan for institutional reforms and upgrades in the 5 years
Study in The recent years tois further clarifyand the Energies 2020, 13, 2065 meaning and research method of the paper; Section 3 introduces the mathematical principles of least squares support vector machine (LSSVM), adaptive differential evolution (ADE) and evolutionary game theory (EGT), and the overall algorithm programming process; in Section 4, the proposed EGT–ADE–LSSVM model is fully trained to reach the level of accurate prediction, and applied to China’s terminal clean electricity through a comparison with the prediction results of a traditional backpropagation neural network (BP), LSSVM and ADE–LSSVM
The results show that the EEMD–LSSVM model is better than the wavelet denoising least squares support vector machine (WD–LSSVM) and the traditional LSSVM
Summary
With the comprehensive deepening of China’s power industry, the “Thirteenth Five-Year Plan”. According to “China Energy Development Report 2019” [3], from the perspective of power generation, in 2019, China’s installed clean energy capacity reached 728 million kilowatts, with an increase of 12%. In order to accurately predict the scale of future terminal clean electricity consumption China. Study in The recent years tois further clarifyand the Energies 2020, 13, 2065 meaning and research method of the paper; Section 3 introduces the mathematical principles of LSSVM, adaptive differential evolution (ADE) and evolutionary game theory (EGT), and the overall algorithm programming process; in Section 4, the proposed EGT–ADE–LSSVM model is fully trained to reach the level of accurate prediction, and applied to China’s terminal clean electricity through a comparison with the prediction results of a traditional backpropagation neural network (BP), LSSVM and ADE–LSSVM algorithms.
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