ULTRA-SHORT-TERM WIND POWER PREDICTION BASED ON TCN-NCP-CFC NEURAL NETWORK

Zhao Fei; Wu Wenbiao; Wang Yanyi

doi:10.19912/j.0254-0096.tynxb.2024-2249

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (4) : 376-386. DOI: 10.19912/j.0254-0096.tynxb.2024-2249

ULTRA-SHORT-TERM WIND POWER PREDICTION BASED ON TCN-NCP-CFC NEURAL NETWORK

Zhao Fei¹, Wu Wenbiao¹, Wang Yanyi²

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Abstract

To address the uncertainties and fluctuations associated with wind power generation, an ultra-short-term wind power forecasting method integrating temporal convolutional networks （TCN）, closed-form continuous-time networks （CFC）, and neural circuit policies （NCP） is proposed. Initially, TCN is utilized to conduct preliminary learning from raw data, extracting crucial information from the time series. Subsequently, the processed data is fed into the NCP-CFC network, which leverages the unique hierarchical brain-like recursive connections of NCP and the efficient solving mechanism and anti-gradient vanishing properties of CFC for forecasting. Finally, a fully connected layer adjusts the output range and dimensions to produce the final forecast. The necessity of each module is validated through ablation studies and comparative experiments with RNN-based models. Two case studies are conducted to demonstrate the effectiveness of the proposed model in ultra-short-term wind power forecasting： one involving a wind farm in Inner Mongolia （MSE=25.70 MW², RMSE=5.07 MW, MAE=3.92 MW, SMAPE=32.51%, R²=0.92） and another using open-source data （MSE=27.38 MW², RMSE=5.23 MW, MAE=3.71 MW, SMAPE=38.52%, R²=0.84）.

Key words

wind power / forecasting / neural network / temporal convolutional network / closed-form continuous-time neural networks / neural circuit policies

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Zhao Fei, Wu Wenbiao, Wang Yanyi. ULTRA-SHORT-TERM WIND POWER PREDICTION BASED ON TCN-NCP-CFC NEURAL NETWORK[J]. Acta Energiae Solaris Sinica. 2026, 47(4): 376-386 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2249

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