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Open AccessArticle

Short-Term Wind Power Prediction Using GA-BP Neural Network Based on DBSCAN Algorithm Outlier Identification

1
School of Mechanical, Electrical and Information Engineering, Shandong University (Weihai), Weihai 264209, China
2
Shandong Hanlin Technology Co., Ltd., Jinan 250000, China
*
Authors to whom correspondence should be addressed.
Processes 2020, 8(2), 157; https://doi.org/10.3390/pr8020157
Received: 5 December 2019 / Revised: 11 January 2020 / Accepted: 21 January 2020 / Published: 27 January 2020
(This article belongs to the Special Issue Multi-Period Optimization of Sustainable Energy Systems)
Accurately predicting wind power plays a vital part in site selection, large-scale grid connection, and the safe and efficient operation of wind power generation equipment. In the stage of data pre-processing, density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to identify the outliers in the wind power data and the collected wind speed data of a wind power plant in Shandong Province, and the linear regression method is used to correct the outliers to improve the prediction accuracy. Considering the important impact of wind speed on power, the average value, the maximum difference and the average change rate of daily wind speed of each historical day are used as the selection criteria to select similar days by using DBSCAN algorithm and Euclidean distance. The short-term wind power prediction is carried out by using the similar day data pre-processed and unprocessed, respectively, as the input of back propagation neural network optimized by genetic algorithm (GA-BP neural network). Analysis of the results proves the practicability and efficiency of the prediction model and the important role of outlier identification and correction in improving the accuracy of wind power prediction.
Keywords: short-term wind power prediction; outlier identification; DBSCAN algorithm; linear regression method; GA-BP neural network short-term wind power prediction; outlier identification; DBSCAN algorithm; linear regression method; GA-BP neural network
MDPI and ACS Style

Zhang, P.; Wang, Y.; Liang, L.; Li, X.; Duan, Q. Short-Term Wind Power Prediction Using GA-BP Neural Network Based on DBSCAN Algorithm Outlier Identification. Processes 2020, 8, 157.

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