Open AccessUltra-short-term Wind Power Output Prediction Based On LS-WMC Combined Model
Author(s) -
Yanan Du,
Zhengning Pang,
Jingxian Qi
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 - 10.1088/1742-6596/2010/1/012112
Subject(s) - weighting , term (time) , markov chain , wind power , computer science , exponential function , power (physics) , mathematics , algorithm , mathematical optimization , statistics , control theory (sociology) , artificial intelligence , engineering , medicine , mathematical analysis , physics , control (management) , quantum mechanics , electrical engineering , radiology
In this paper, an ultra short term wind power output prediction method based on least square and weighted Markov chain model is proposed. Firstly, the wind farm output is fitted based on the least square method, and then the prediction accuracy is improved by using the weighted Markov chain model. At the same time, the partial exponential weighting method is used to improve the prediction accuracy of ultra short-term wind power output on the basis of reducing the generation frequency of transition probability matrix, so as to optimize and improve the prediction results. The results show that the method can improve the prediction accuracy.