Energy is vital for the sustainable development of China. Accurate forecasts of annual energy demand are essential to schedule\nenergy supply and provide valuable suggestions for developing related industries. In the existing literature on energy use prediction,\nthe artificial intelligence-based (AI-based) model has received considerable attention. However, few econometric and statistical\nevidences exist that can prove the reliability of the current AI-based model, an area that still needs to be addressed. In this study, a\nnew energy demand forecasting framework is presented at first. On the basis of historical annual data of electricity usage over the\nperiod of 1985ââ?¬â??2015, the coefficients of linear and quadratic forms of the AI-based model are optimized by combining an adaptive\ngenetic algorithm and a cointegration analysis shown as an example. Prediction results of the proposed model indicate that the\nannual growth rate of electricity demand in China will slow down. However, China will continue to demand about 13 trillion\nkilowatt hours in 2030 because of population growth, economic growth, and urbanization. In addition, the model has greater\naccuracy and reliability compared with other single optimization methods.
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