Prospect Prediction of Terminal Clean Power Consumption in China via LSSVM Algorithm Based on Improved Evolutionary Game Theory

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.

Keywords

• terminal clean power consumption forecast
• grey relevance theory
• evolutionary game theory
• adaptive differential evolution
• least squares support vector machine