Prediction of ultra-short-term wind power based on CEEMDAN-LSTM-TCN. (November 2022)

• Record Type: Journal Article
• Authors: Hu, Chenjia; Zhao, Yan; Jiang, He; Jiang, Mingkun; You, Fucai; Liu, Qian
• Is Part Of: Energy reports. Volume 8(2022)Supplement 8
• Page Start: 483
• Page End: 492
• Publication Date: 2022-11
• Subjects: Wind power generation system -- Wind power -- Renewable energy -- Ultra short-term wind power forecast; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2022.09.171
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: 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.