Energy Demand Forecasting: Combining Cointegration Analysis and Artificial Intelligence Algorithm

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Huang, Junbing ; Tang, Yuee ; Chen, Shuxing (2018)
  • Publisher: Hindawi
  • Journal: Mathematical Problems in Engineering (issn: 1024-123X, eissn: 1563-5147)
  • Related identifiers: doi: 10.1155/2018/5194810
  • Subject: TA1-2040 | Mathematics | Engineering (General). Civil engineering (General) | QA1-939 | Article Subject

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