Short-Term Wind Power Forecasting and Uncertainty Analysis Based on Hybrid Temporal Convolutional Network

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dc.creatorJian, Yang
dc.creatorShuai, Yao
dc.creatorXuejun, Chang
dc.creatorDewei, Li
dc.creatorBo, Gu
dc.creatorZichao, Zhang (TTU)
dc.date.accessioned2023-08-16T14:38:54Z
dc.date.available2023-08-16T14:38:54Z
dc.date.issued2023
dc.description© 2023 School of Science, IHU. All rights reserved. cc-by-nc
dc.description.abstractThe integration of large-scale wind power into power grids has made accurate short-term wind power forecasting a key technology for the safe and economical operation of power grids. A novel method based on variational mode decomposition (VMD), temporal convolutional network (TCN), and Gaussian mixture model (GMM) was proposed for accurate short-term wind power forecasting and uncertainty analysis. First, the wind speed information was decomposed into different mode components via VMD. Second, TCN was employed to capture accurately the time-series dependence of data by training and forecasting different mode component data. On this basis, GMM was used to calculate the distribution characteristics of short-term wind power forecasting errors and quantify the confidence interval of wind power forecasting. Results demonstrated that the root mean square error (RMSE) value of the VMD-TCN model for wind power forecasting for 4 h during winter is 4.69%, 3.13%, 2.48%, 1.21%, and 0.7% lower than the RMSE values of wavelet neural network, BP neural network, PSO-BP hybrid model, long short-term memory model, and TCN model, respectively. The proposed method has a certain promoting effect on improving the accuracy of short-term wind power forecasting.
dc.identifier.citationJian, Y., Shuai, Y., Xuejun, C., Dewei, L., Bo, G., & Zichao, Z.. 2023. Short-Term Wind Power Forecasting and Uncertainty Analysis Based on Hybrid Temporal Convolutional Network. Journal of Engineering Science and Technology Review, 16(2). https://doi.org/10.25103/jestr.162.24
dc.identifier.urihttps://doi.org/10.25103/jestr.162.24
dc.identifier.urihttps://hdl.handle.net/2346/95633
dc.language.isoeng
dc.subjectConfidence interval
dc.subjectGaussian mixture model
dc.subjectShort-term wind power forecasting
dc.subjectTemporal convolutional networks
dc.subjectVariational mode decomposition
dc.titleShort-Term Wind Power Forecasting and Uncertainty Analysis Based on Hybrid Temporal Convolutional Network
dc.typeArticle

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