SHORT-TERM WIND POWER PREDICTION BASED ON DEEP BELIEF NETWORK

Yuan Guili; Wu Zhenmin; Liu Huaqi; Yu Jianfang; Fang Fang

doi:10.19912/j.0254-0096.tynxb.2020-0405

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (2) : 451-457. DOI: 10.19912/j.0254-0096.tynxb.2020-0405

SHORT-TERM WIND POWER PREDICTION BASED ON DEEP BELIEF NETWORK

Yuan Guili, Wu Zhenmin, Liu Huaqi, Yu Jianfang, Fang Fang

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Abstract

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.

Key words

deep learning / wind power prediction / unsupervised learning / cluster analysis / discriminant analysis

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Yuan Guili, Wu Zhenmin, Liu Huaqi, Yu Jianfang, Fang Fang. SHORT-TERM WIND POWER PREDICTION BASED ON DEEP BELIEF NETWORK[J]. Acta Energiae Solaris Sinica. 2022, 43(2): 451-457 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0405

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