A unified multi-step wind speed forecasting framework based on numerical weather prediction grids and wind farm monitoring data

Liu, Xingdou, Zhang, Li, Wang, Jiangong, Zhou, Yue ORCID: https://orcid.org/0000-0002-6698-4714 and Gan, Wei 2023. A unified multi-step wind speed forecasting framework based on numerical weather prediction grids and wind farm monitoring data. Renewable Energy 211 , pp. 948-963. 10.1016/j.renene.2023.05.006

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Abstract

Wind speed forecasting is the basis of wind farm operation, which provides a reference for the future operation status evaluation of wind farms. For the wind speed forecast of wind turbines in the whole wind farm, a strategy combining unified forecast and single site error correction is proposed in this paper. The unified forecast framework is composed of a unified forecast model and multiple single site error correction models, which combines the forecasted grids of numerical weather prediction (NWP) with the monitoring data of wind farms. The proposed unified forecast model is called spatiotemporal conversion deep predictive network (STC-DPN), which is composed of temporal convolution network (TCN) and 2D convolution long short-term memory network (ConvLSTM). Firstly, the NWP forecasted grids are interpolated to the fan location, and the sequence matrix is composed of the NWP data and the monitored data of each wind turbine according to the time series, which is entered into the TCN network for time sequence feature extraction. Then, the output of the TCN network is converted into a regular spatio-temporal data matrix, which is entered into the ConvLSTM network for joint learning of spatio-temporal features to obtain the wind speed sequence forecasted in the whole wind farm. Finally, an independent TCN-LSTM error correction model is added for each site. Variational modal decomposition (VMD) is used to process data series, and different processing methods are adopted in unified forecast and single site error correction. In the 96 steps forecast test of a wind farm from Jining City, China, the proposed method is superior to several baseline methods and has important practical application value.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0960-1481
Date of First Compliant Deposit:	23 June 2023
Date of Acceptance:	1 May 2023
Last Modified:	02 Jul 2023 06:17
URI:	https://orca.cardiff.ac.uk/id/eprint/160517

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