Open AccessResearch on Short-term Wind Power Forecasting Based on Stacking Integrated Model
Author(s) -
Haoyue Wang,
Haitao Zhao
Publication year - 2022
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 - 10.1088/1742-6596/2247/1/012022
Subject(s) - wind power , computer science , wind speed , wind power forecasting , generalization , extreme learning machine , artificial neural network , wavelet , turbine , decision tree , electric power system , mathematical optimization , control theory (sociology) , artificial intelligence , power (physics) , engineering , mathematics , meteorology , mechanical engineering , mathematical analysis , physics , control (management) , quantum mechanics , electrical engineering
With the large-scale use of wind farms, wind power has become an indispensable energy source. However, due to the influence of internal instability factors of wind, it poses a huge threat to the economic efficiency and reliability of power system operation. In order to solve this problem, according to the random volatility of wind power, this paper establishes a wavelet threshold noise reduction model to process wind speed characteristics. Because the wind power is very different when the wind speed is the same, many of the wind turbine’s own attributes are added to the input characteristics. Based on the strong generalization ability of decision tree models, the gradient descent tree, extreme gradient boost tree, and lightGBM algorithm are used as the base learner, and the multiple linear regression model is used as the meta-learner to construct a stacking integrated learning model. Compared with methods such as long and short-term memory neural networks, the performance and generalization ability of the cascading generalization algorithm combination model is relatively good.