DOI:10.35833/MPCE.2020.000874 |
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Performance Improvement of Very Short-term Prediction Intervals for Regional Wind Power Based on Composite Conditional Nonlinear Quantile Regression |
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Page view: 205
Net amount: 582 |
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Author:
Yan Zhou1,Yonghui Sun1,Sen Wang1,Rabea Jamil Mahfoud1,Hassan Haes Alhelou2,Nikos Hatziargyriou3,Pierluigi Siano4
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Author Affiliation:
1.College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China;2.Department of Electrical Engineering, Tishreen University, Latakia 2230, Syria;3.National Technical University of Athens, Athens 15773, Greece;4.Department of Management & Innovation Systems, University of Salerno, Salerno 84084, Italy
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Foundation: |
This work was supported by the National Key R&D Program of China “Technology and Application of Wind Power/Photovoltaic Power Prediction for Promoting Renewable Energy Consumption” (No. 2018YFB0904200) and Complement S&T Program of State Grid Corporation of China (No. SGLNDKOOKJJS1800266). |
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Abstract: |
Accurate regional wind power prediction plays an important role in the security and reliability of power systems. For the performance improvement of very short-term prediction intervals (PIs), a novel probabilistic prediction method based on composite conditional nonlinear quantile regression (CCNQR) is proposed. First, the hierarchical clustering method based on weighted multivariate time series motifs (WMTSM) is studied to consider the static difference, dynamic difference, and meteorological difference of wind power time series. Then, the correlations are used as sample weights for the conditional linear programming (CLP) of CCNQR. To optimize the performance of PIs, a composite evaluation including the accuracy of PI coverage probability (PICP), the average width (AW), and the offsets of points outside PIs (OPOPI) is used to quantify the appropriate upper and lower bounds. Moreover, the adaptive boundary quantiles (ABQs) are quantified for the optimal performance of PIs. Finally, based on the real wind farm data, the superiority of the proposed method is verified by adequate comparisons with the conventional methods. |
Keywords: |
Regional wind power ; probabilistic prediction ; nonlinear quantile regression ; composite evaluation ; adaptive boundary quantiles |
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Received:December 17, 2020
Online Time:2022/01/28 |
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