Prediction of ultra-short-term wind power based on CEEMDAN-LSTM-TCN

• Published in: Energy reports Vol. 8; pp. 483 - 492
• Main Authors: Hu, Chenjia, Zhao, Yan, Jiang, He, Jiang, Mingkun, You, Fucai, Liu, Qian
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2022; Elsevier
• Subjects: Renewable energy; Ultra short-term wind power forecast; Wind power; Wind power generation system; Ultra short-term wind power forecast; Wind power generation system; Renewable energy; Wind power
• ISSN: 2352-4847; 2352-4847
• DOI: 10.1016/j.egyr.2022.09.171

So as to decrease those cacoethic impact of a huge amount of wind energy generation systems associated with the electric power system and improve the utilization rate and the budgetary profits of wind power era, this paper raises a neural network in view of CEEMDAN-LSTM-TCN. Firstly, CEEMDAN is used to break down the wind velocity arrangement to decrease the sway of arbitrariness Furthermore variance about wind velocity. Secondly, the ultra-short-term wind power forecast depend upon LSTM and TCN is built to realize the real-time prediction for wind energy. Finally, the simulation results show that LSTM-TCN can deal with multi time order characteristics and predict ultra-short period wind energy with effect, which is better than LSTM and TCN. It also has a scientific reference for local power dispatching.