Open AccessMedium and long term wind power generation forecast based on OWA combined model and Markov chain
Author(s) -
Ze Gong,
Qifeng Xu,
Nan Xie
Publication year - 2021
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 - 10.1088/1742-6596/2005/1/012148
Subject(s) - markov chain , term (time) , wind power , computer science , renewable energy , wind power forecasting , mathematical optimization , markov model , markov process , power (physics) , machine learning , electric power system , mathematics , statistics , engineering , physics , quantum mechanics , electrical engineering
Wind energy, as one of the renewable energies with the most potential for development, has been widely concerned. At the same time, the medium and long term wind power prediction is easily affected by many factors. In order to avoid the instability of a single model, this paper first builds a self-adaptive filtering model and a gray model with parameter optimized by teaching learning-based optimization (TLBO), then uses ordered weighted averaging (OWA) to assign weights to two single models, and finally uses Markov chain to modify the prediction results to further improve the prediction accuracy.