Short-term wind power prediction based on deep belief network

被引：0
|
作者：
Yuan G. [1 ]
Wu Z. [1 ]
Liu H. [1 ]
Yu J. [1 ]
Fang F. [1 ]
机构：
[1] School of Control and Computer Engineering, North China Electric Power University, Beijing
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2022年 / 43卷 / 02期
关键词：
Cluster analysis; Deep learning; Discriminant analysis; Unsupervised learning; Wind power prediction;
D O I：
10.19912/j.0254-0096.tynxb.2020-0405
中图分类号：
学科分类号：
摘要：
The current massive wind power historical data provides a very good foundation for the use of deep learning for wind power prediction. In order to solve the problem of information redundancy due to the massive data used as the training sample of the prediction model, a short-term wind power prediction method based on Deep Belief Network is proposed. The method first uses historical data as training samples, extracts its corresponding features through deep belief network unsupervised learning, and then uses the K-means algorithm to perform cluster analysis on the extracted features. The historical data is divided into several categories, and the category of the days to be measured is determined by discriminant analysis. Using the historical data belong to this category to supervised train the Deep Belief Network with the error feedback layer, and inputting the weather information of the day to be measured into the trained Deep Belief Network model, the predicted power is finally obtained. The effectiveness of the proposed method is verified by an example of the actual operation data of a wind farm in Yunnan province. © 2022, Solar Energy Periodical Office Co., Ltd. All right reserved.
引用
收藏
页码：451 / 457
页数：6
相关论文
共 20 条
  • [1] QIAO Y, LU Z X, MIN Y., Research & application of raising wind power prediction accuracy, Power system technology, 41, 10, pp. 3261-3268, (2017)
  • [2] CHEN W, WU B T, PEI X P., Classification multi-modelalgorithm for abnormal data preprocessing in wind turbines, Proceedings of the CSU-EPSA, 30, 4, pp. 137-143, (2018)
  • [3] PENG W, XIE F Y, ZHANG Z Y., Short-term wind power forecasting algorithm based on similar time period clustering, Proceedings of the CSU-EPSA, 31, 10, pp. 81-87, (2019)
  • [4] WANG S Z, GAO S, ZHAO X, Et al., A multi-timescale wind power forecasting method based on selection of similar days, China International Conference on Electricity Distribution, (2016)
  • [5] LUAN Y, YANG Y Q, YAN W L., Research on short-term forecast of wind power based on similar day and artificial neural network, China energy and environmental protection, 40, 10, pp. 140-146, (2018)
  • [6] DING Z Y, YANG P, YANG X, Et al., Wind power prediction method based on sequential time clustering support vector machine, Automation of electric power systems, 36, 14, pp. 131-135, (2012)
  • [7] WANG B, FENG S L, LIU C., Study on weather typing based wind power prediction, Power system technology, 38, 1, pp. 93-98, (2014)
  • [8] HINTON G E, OSINDERO S, TEH Y W., A fast learning algorithm for deep belief nets, Neural computation, 18, 7, pp. 1527-1554, (2006)
  • [9] SUN Z J, XUE L, XU Y M, Et al., Overview of deep learnin, Application research of computers, 29, 8, pp. 2806-2810, (2012)
  • [10] YANG H T, YANG W Z, YIN Y B, Et al., Research on K-means clustering algorithm based on deep belief network, Modern electronics technique, 42, 8, pp. 145-150, (2019)
12