Exploration of energy saving potential in China power industry based on Adaboost back propagation neural network

The power industry of China is an energy-intensive one. In recent years, the demand for energy-saving oriented researches has been increased to provide recommendations to the final energy abatement policy making for the industry. Hence, a study which analyzes the typical energy consumption influencing factors and the potential energy-saving capability for the current power industry of China is needed. In this paper, the energy consumption significant influence factors have been picked out and the potential energy-saving capability has been estimated through the analysis conducted under several different scenarios. The results of this work can contribute to the assessment of energy abatement spaces for the power industry of China from now to the year of 2025. At the beginning of the study, 8 factors including social, economic and technical aspects which affect the energy consumption significantly have been selected by Back Propagation Mean Impact Value (BP-MIV) network approach. Then the 8 filtered factors are employed as input in the Back Propagation Adaptive Boosting (Adaboost-BP) neural network model. The Adaboost-BP was trained and tested on existing data set. Being compared with the results on the same data set from grey prediction and regular BP network, the results of Adaboost-BP showed better accuracy over the other two methods. In the final step of this work, the potential energy-saving capability of China's power industry through the year 2025 was predicted using the trained Adaboost-BP network under 3 different scenarios. The results show that the energy consumption estimates can reach at most 319.657 million tons of standard coal under conventional condition at the year of 2025, with 307.8087 and 294.0182 million tons under moderate and advanced conditions respectively. This means that, 11.8483 million tons and 25.6388 million tons of standard coal could be potentially saved at the year of 2025 under moderate scenario and advanced scenario respectively. This work also presented a guideline for energy saving policies making for the power industry of China. (C) 2019 Elsevier Ltd. All rights reserved.