Abstract
In order to predict the potential of electric energy substitution in the next decade in China, this paper proposes a prediction method based on Logistic curve fitting and improved BP neural network algorithm. The amount of electric energy substitution is defined to quantify the potential of electric energy substitution. Then the important influencing factors of electric energy substitution based on the Impact by Population, Affluence and Technology (IPAT) model are established and quantified. For different influencing factors, logistic curve fitting and polynomial function fitting method were used to estimate the data fitting. A two-node output layer model of BP neural network is established and improved with additional momentum factors and adaptive learning rate to learn and train the data related to electric energy substitution from 2003 to 2017, and calculate the amount of electric energy substitution which are substitution potential from 2018 to 2020, 2025 and 2030. The calculation results show that the method has higher computational accuracy and fewer iterations. The prediction results are reasonable and effective, which can be the reference of the research of energy substitution.
Highlights
With the development of productivity, human consumption and dependence on fossil energy is increasing
This paper studies the issue of electrical energy substitution and its potential prediction
The electric energy substitution is classified in different styles, and the electric energy substitution quantity model is established to quantify the substitution potential
Summary
With the development of productivity, human consumption and dependence on fossil energy is increasing. In view of the prediction of electric energy substitution potential, domestic scholars have proposed many research methods. In reference [5], a research model of electric energy substitution potential based on Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) is proposed to quantitatively analyse the relationship between the potential of electric energy substitution and its influencing factors. Learning from the above methods, this paper combines Logistic curve fitting and back propagation (BP) network learning algorithm to predict the potential of electric energy substitution. The IPAT model is improved and the quantitative factors affecting the Electric energy substitution by Population, Affluence, Technology and Regulation (EPATR) model are established. The prediction results are compared with the time series prediction method and the least squares prediction method to verify its rationality
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
