Targeting at the problem of imprecise power prediction of wind power generation caused by weather fluctuations and nonlinear features, a new hybrid approach for wind farm power prediction is proposed by a combination of convolutional neural network (CNN), bi-directional long and short-term memory network (BiLSTM) network, and Whale Optimization Algorithm (WOA). The model takes into account both the spatial and the temporal features of the sequence data. The input layer feeds in a large amount of historical power generation, weather and date information, the CNN layer extracts the spatial features, and then the forward and reverse sequences of the data are processed by the BiLSTM layer to capture long term temporal dependence. Meanwhile, the global search of WOA is utilized to optimize the parameters of the CNN-BiLSTM model, and finally, the WOA-CNN-BiLSTM model to predict the wind power sequence. The experiments utilize authentic wind power generation data supplied by Longyuan Power Group Corporation, and the RMSE sum of the experimental results are compared and analyzed by comparing the BP neural network, LSTM, BiLSTM, CNN-LSTM, and BiLSTM-Attention models. The results show that the RMSE of this hybrid model is 4.425, is 0.965, and the root mean square error is decreased by ${2. 8 \%}$ in comparison to the conventional LSTM model, resulting in improved prediction accuracy and stability.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Research and Application of WOA-CNN-BiLSTM Based Model for Wind Power Prediction


    Beteiligte:
    Cong, Boyu (Autor:in) / Ma, Shun (Autor:in) / Li, Shenshen (Autor:in)


    Erscheinungsdatum :

    23.10.2024


    Format / Umfang :

    1833530 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Carbon Price Prediction based on EMD-BiLSTM-ATTENTION model

    Li, Tong / Li, Shilun / Lin, Feng et al. | IEEE | 2023


    Attention-BiLSTM-based ship track prediction method

    YANG ZE / GUAN ZHENBO / GE XIANG et al. | Europäisches Patentamt | 2025

    Freier Zugriff


    Research on CNN-BiLSTM Power Load Forecasting Based on VMD Algorithm

    Dai, Yongsheng / Wang, Rongrong / Ma, Yahong et al. | IEEE | 2023


    Taxi-Passenger’s Destination Prediction via GPS Embedding and Attention-Based BiLSTM Model

    Liao, Chengwu / Chen, Chao / Xiang, Chaocan et al. | IEEE | 2022