Please use this identifier to cite or link to this item: http://hdl.handle.net/11328/3707
Title: An ultra-short-term wind speed forecasting model based on time scale recognition and dynamic adaptive modeling
Authors: Zhen, Zhao
Qiu, Gang
Shengwei, Mei
Wang, Fei
Zhang, Xuemin
Yin, Rui
Li, Yu
Osório, Gerardo J.
Shafie-khah, Miadreza
Catalão, João P. S.
Keywords: Wind speed forecast
Wind process
Time scale distribution function
Pattern recognition
Complex network
Issue Date: Feb-2022
Publisher: Elsevier
Citation: Zhen, Z., Qiu, G., Mei, S., Wang, F., Zhang, X., Yin, R., Li, Y, Osório, G. J., Shafie-khah, M., & Catalão, J. P. S. (2022). An ultra-short-term wind speed forecasting model based on time scale recognition and dynamic adaptive nodeling. International Journal of Electrical Power & Energy Systems, , 135(107502). Doi: 10.1016/j.ijepes.2021.107502. Disponível no Repositório UPT, http://hdl.handle.net/11328/3707
Abstract: The forecast of wind speed is a prerequisite for wind power prediction, which is one of the most effective means of promoting wind power absorption. However, when modeling for wind speed sequences with different fluctuations, most existing researchers ignore the influence of the time scale of wind speed fluctuation period, let alone the low compatibility between training and testing samples that severely limit the training performance of the forecasting model. To improve the accuracy of wind speed and wind power forecasting, an ultra-short-term wind speed forecasting model based on time scale recognition and dynamic adaptive modeling is proposed in this paper. First, a series of wind processes are divided from the historical wind speed sequence according to the natural variation characteristics of wind speed. Second, we divide all the wind processes into two patterns based on their time scale, and an SVC model with input features extracted from meteorological data is built to identify the time scale of the current wind process. Third, for a specifically identified wind process, the complex network algorithm is applied in data screening to select high compatible training samples to train the forecast model dynamically for current input. The simulation indicates that the proposed approach presents higher accuracy than benchmark models using the same forecasting algorithms but without considering the time scale and data screening.
URI: http://hdl.handle.net/11328/3707
ISSN: 0142-0615
Appears in Collections:REMIT – Artigos em Revistas Internacionais / Papers in International Journals

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