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A New Wind Speed Evaluation Method Based on Pinball Loss and Winkler ScoreLI, G., ZHANG, J.   , SHEN, X. , KONG, C. , ZHANG, Y. |
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Author keywords
wind speed forecasting, variational modal decomposition, neural network, Monte Carlo-Markov Chain stimulation, Winkler score
References keywords
wind(29), energy(24), speed(19), forecasting(19), term(12), short(12), prediction(11), power(9), model(9), applied(9)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2022-05-31
Volume 22, Issue 2, Year 2022, On page(s): 11 - 18
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2022.02002
Web of Science Accession Number: 000810486800002
SCOPUS ID: 85131767763
Abstract
To reduce the adverse effects of the inherent stochastic volatility and uncontrollability of new energy on wind energy forecasting, this paper starting from the two aspects of improving the deterministic forecast and enhancing the predictability of volatility risk, the combination of variational modal decomposition (VMD), neural network and statistical model is applied to point forecasting, and the forecast model selection is based on the statistical characteristics of the components to enhance the degree of preciseness of wind speed forecasting. Then Monte Carlo-Markov Chain (MCMC) stimulation based on different quantiles is proposed to make interval prediction, and a new interval evaluation method is introduced, pinball loss function and Winkler score, to select the best interval prediction results for achieving precise control of wind power within a certain period of time. Finally, through experimental case verification, the performance of the advanced hybrid deterministic forecasting model is more advantageous than that of the traditional model. At the same time, the proposed interval prediction method better quantifies the uncertainty risk of wind power, makes up for the lack of a single evaluation method in the current interval prediction research, and can provide information support for the stable operation. |
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[1] An integrated method with adaptive decomposition and machine learning for renewable energy power generation forecasting, Li, Guomin, Yu, Leyi, Zhang, Ying, Sun, Peng, Li, Ruixuan, Zhang, Yagang, Li, Gengyin, Wang, Pengfei, Environmental Science and Pollution Research, ISSN 1614-7499, Issue 14, Volume 30, 2023.
Digital Object Identifier: 10.1007/s11356-023-25194-3 [CrossRef]
[2] Hybrid forecasting system considering the influence of seasonal factors under energy sustainable development goals, Li, Guomin, Pan, Zhiya, Qi, Zihan, Wang, Hui, Wang, Tao, Zhao, Yunpeng, Zhang, Yagang, Li, Gengyin, Wang, Pengfei, Measurement, ISSN 0263-2241, Issue , 2023.
Digital Object Identifier: 10.1016/j.measurement.2023.112607 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
