Open AccessEstablishment of Clean Energy Demand Forecasting Model in China Based on Genetic Algorithms
Author(s) -
Jiying Chen,
Gaoyuan Cheng,
Shuanghe Chi
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/384/1/012143
Subject(s) - energy consumption , computer science , demand forecasting , energy (signal processing) , energy supply , supply and demand , genetic algorithm , consumption (sociology) , energy demand , elasticity (physics) , econometrics , environmental economics , economics , mathematical optimization , operations research , engineering , microeconomics , statistics , social science , materials science , mathematics , sociology , electrical engineering , composite material , machine learning
In the future, energy development will face a series of severe challenges, such as insufficient domestic conventional energy resources, huge oil supply gap, large amount of clean energy needed by cities, and huge international pressure on global climate change. This paper uses historical data and genetic algorithm to improve it, and uses function expansion model to reveal the internal proportional connection between energy consumption and economic development. By comparing the forecasting methods of clean energy demand, it is found that the energy consumption elasticity coefficient method is used to measure and predict the growth ratio of energy consumption, and the figure in the next few years is measured. The software has self-built optimization model, reflects energy supply and demand fluctuations, and generates reports. It can make decision support for regional clean energy system planning, miscalculation and clean energy demand forecasting.