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Open AccessArticle

Research on Short-Term Wind Power Forecasting by Data Mining on Historical Wind Resource

by Bin Tang 1, Yan Chen 1,2,*, Qin Chen 1 and Mengxing Su 1
1
Key Laboratory of Intelligent Manufacturing Technology, Ministry of Education, Shantou University, Shantou 515063, China
2
Institute of Energy Science, Shantou University, Shantou 515063, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(4), 1295; https://doi.org/10.3390/app10041295
Received: 11 January 2020 / Revised: 11 February 2020 / Accepted: 11 February 2020 / Published: 14 February 2020
(This article belongs to the Special Issue Machine Learning for Energy Forecasting)
In order to enhance the accuracy of short-term wind power forecasting (WPF), a short-term wind power forecasting method based on historical wind resources by data mining has been designed. Firstly, the spoiled data resulting from wind turbine and meteorological monitoring equipment is eliminated, and the missing data is added by the Lomnaofski optimization model, which is based on the temporal-spatial correlation of meteorological data. Secondly, the wind characteristics are analyzed by the continuous time similarity clustering (CTSC) method, which is used to select similar samples. To improve the accuracy of deterministic prediction and prediction error, the radial basis function neural network (RBF) deterministic forecasting model was built, which can approximate nonlinear solutions. In addition, the wind power interval prediction method, combining fuzzy information granulation and an Elman neural network (FIG-Elman), is proposed to acquire forecasting intervals. The deterministic prediction of the RBF-CTSC model has high accuracy, which can accurately describe the randomness, fluctuation and nonlinear characteristics of wind speed. Additionally, the mean absolute error (MAE) and root mean square error (RMSE) are reduced by the new model. The interval prediction of FIG-Elman results show that the interval width decreased by 18.85%, and the coverage probability of interval increased by 10.94%.
Keywords: Lomnaofski norm; RBF neural network; FIG-Elman network; deterministic wind power predication; wind power interval predication Lomnaofski norm; RBF neural network; FIG-Elman network; deterministic wind power predication; wind power interval predication
MDPI and ACS Style

Tang, B.; Chen, Y.; Chen, Q.; Su, M. Research on Short-Term Wind Power Forecasting by Data Mining on Historical Wind Resource. Appl. Sci. 2020, 10, 1295.

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