Open AccessShort-term Power Forecast of Wind Power Generation Based on Genetic Algorithm Optimized Neural Network
Author(s) -
Canzong Zhou,
Bin Tang,
Wei Cui,
Zhengmao Yao
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1601/2/022046
Subject(s) - wind power , renewable energy , wind power forecasting , genetic algorithm , artificial neural network , power (physics) , electricity generation , computer science , environmental science , meteorology , electric power system , environmental economics , engineering , electrical engineering , economics , geography , artificial intelligence , machine learning , physics , quantum mechanics
With the rapid development of economy, human consumption of fossil energy has caused a global energy crisis. Nowadays, countries all over the world are making great efforts to develop clean energy. Wind energy as one of the pollution-free, renewable and high-quality clean energy, has attracted widespread attention. However, wind power generation is easily affected by natural factors and is instable. In order to improve the quality of wind power, this paper analyzes the influencing factors of wind power, studies the prediction method of wind power forecasting, and uses genetic algorithm optimization neural network to forecast the wind power of a wind farm in Northwest China, which may provide some reference for the power generation and grid connection of wind power plants.