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With the highest energy growth rate and the fastest economic growth, China has more serious concerns about supplying energy to its economy. Therefore, one of the methods of energy management in this country is to forecast energy demand for the coming years. The main purpose of this study is to predict energy demand under the influence of socio-economic indicators by Artificial Neural Network (A = NN). More accurate forecasts make more reliable decisions to manage energy. For this purpose, in this study, innovation in the ANN method has been presented. This innovation is to use a developed version of a newly introduced metaheuristic. This developed algorithm increases the accuracy of prediction by solving optimization problems. This algorithm is a newly developed version of the Search and Rescue (SAR) algorithm. The results of predictions showed that the parameters population and GDP (per capita) have the highest correlation in train and test steps. The correlation between population and GDP (per capita) in training are  R2  = 0.675 and  R2  = 0.652, respectively. Also, the correlation between population and GDP in testing are  R2  = 0.624 and  R2  = 0.519, respectively. The results also showed that in the coming years, society will face an upward trend in demand.

Prediction of energy demand by socio-economic and climatic indicators using ANN optimised by developed search and rescue (DSAR) algorithm