Prediction of primary energy demand in China based on AGAEDE optimal model

In this article, we present an application of Adaptive Genetic Algorithm Energy Demand Estimation (AGAEDE) optimal model to improve the efficiency of energy demand prediction. The coefficients of the two forms of the model (both linear and quadratic) are optimized by AGA using factors, such as GDP, population, urbanization rate, and R&D inputs together with energy consumption structure, that affect demand. Since the spurious regression phenomenon occurs for a wide range of time series analysis in econometrics, we also discuss this problem for the current artificial intelligence model. The simulation results show that the proposed model is more accurate and reliable compared with other existing methods and the China's energy demand will be 5.23 billion TCE in 2020 according to the average results of the AGAEDE optimal model. Further discussion illustrates that there will be great pressure for China to fulfill the planned goal of controlling energy demand set in the National Energy Demand Project (2014-2020).