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  • Open Access


    Wind Speed Prediction Using Chicken Swarm Optimization with Deep Learning Model

    R. Surendran1,*, Youseef Alotaibi2, Ahmad F. Subahi3

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3371-3386, 2023, DOI:10.32604/csse.2023.034465

    Abstract High precision and reliable wind speed forecasting have become a challenge for meteorologists. Convective events, namely, strong winds, thunderstorms, and tornadoes, along with large hail, are natural calamities that disturb daily life. For accurate prediction of wind speed and overcoming its uncertainty of change, several prediction approaches have been presented over the last few decades. As wind speed series have higher volatility and nonlinearity, it is urgent to present cutting-edge artificial intelligence (AI) technology. In this aspect, this paper presents an intelligent wind speed prediction using chicken swarm optimization with the hybrid deep learning (IWSP-CSODL) method. The presented IWSP-CSODL model… More >

  • Open Access


    Spatio-temporal Model Combining VMD and AM for Wind Speed Prediction

    Yingnan Zhao1,*, Peiyuan Ji1, Fei Chen1, Guanlan Ji1, Sunil Kumar Jha2

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1001-1016, 2022, DOI:10.32604/iasc.2022.027710

    Abstract This paper proposes a spatio-temporal model (VCGA) based on variational mode decomposition (VMD) and attention mechanism. The proposed prediction model combines a squeeze-and-excitation network to extract spatial features and a gated recurrent unit to capture temporal dependencies. Primarily, the VMD can reduce the instability of the original wind speed data and the attention mechanism functions to strengthen the impact of important information. In addition, the VMD and attention mechanism act to avoid a decline in prediction accuracy. Finally, the VCGA trains the decomposition result and derives the final results after merging the prediction result of each component. Contrasting experiments for… More >

  • Open Access


    Spatio-Temporal Wind Speed Prediction Based on Variational Mode Decomposition

    Yingnan Zhao1,*, Guanlan Ji1, Fei Chen1, Peiyuan Ji1, Yi Cao2

    Computer Systems Science and Engineering, Vol.43, No.2, pp. 719-735, 2022, DOI:10.32604/csse.2022.027288

    Abstract Improving short-term wind speed prediction accuracy and stability remains a challenge for wind forecasting researchers. This paper proposes a new variational mode decomposition (VMD)-attention-based spatio-temporal network (VASTN) method that takes advantage of both temporal and spatial correlations of wind speed. First, VASTN is a hybrid wind speed prediction model that combines VMD, squeeze-and-excitation network (SENet), and attention mechanism (AM)-based bidirectional long short-term memory (BiLSTM). VASTN initially employs VMD to decompose the wind speed matrix into a series of intrinsic mode functions (IMF). Then, to extract the spatial features at the bottom of the model, each IMF employs an improved convolutional… More >

  • Open Access


    Design of Neural Network Based Wind Speed Prediction Model Using GWO

    R. Kingsy Grace1,*, R. Manimegalai2

    Computer Systems Science and Engineering, Vol.40, No.2, pp. 593-606, 2022, DOI:10.32604/csse.2022.019240

    Abstract The prediction of wind speed is imperative nowadays due to the increased and effective generation of wind power. Wind power is the clean, free and conservative renewable energy. It is necessary to predict the wind speed, to implement wind power generation. This paper proposes a new model, named WT-GWO-BPNN, by integrating Wavelet Transform (WT), Back Propagation Neural Network (BPNN) and Grey Wolf Optimization (GWO). The wavelet transform is adopted to decompose the original time series data (wind speed) into approximation and detailed band. GWO – BPNN is applied to predict the wind speed. GWO is used to optimize the parameters… More >

  • Open Access


    Short-term Wind Speed Prediction with a Two-layer Attention-based LSTM

    Jingcheng Qian1, Mingfang Zhu1, Yingnan Zhao2,*, Xiangjian He3

    Computer Systems Science and Engineering, Vol.39, No.2, pp. 197-209, 2021, DOI:10.32604/csse.2021.016911

    Abstract Wind speed prediction is of great importance because it affects the efficiency and stability of power systems with a high proportion of wind power. Temporal-spatial wind speed features contain rich information; however, their use to predict wind speed remains one of the most challenging and less studied areas. This paper investigates the problem of predicting wind speeds for multiple sites using temporal and spatial features and proposes a novel two-layer attention-based long short-term memory (LSTM), termed 2Attn-LSTM, a unified framework of encoder and decoder mechanisms to handle temporal-spatial wind speed data. To eliminate the unevenness of the original wind speed,… More >

  • Open Access


    Wind Speed Prediction Modeling Based on the Wavelet Neural Network

    Zhenhua Guo1,2, Lixin Zhang1,*, Xue Hu1, Huanmei Chen2

    Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 625-630, 2020, DOI:10.32604/iasc.2020.013941

    Abstract Wind speed prediction is an important part of the wind farm management and wind power grid connection. Having accurate prediction of short-term wind speed is the basis for predicting wind power. This paper proposes a short-term wind speed prediction strategy based on the wavelet analysis and the multilayer perceptual neural network for the Dabancheng area, in China. Four wavelet neural network models using the Morlet function as the wavelet basis function were developed to forecast short-term wind speed in January, April, July, and October. Predicted wind speed was compared across the four models using the mean square error and regression.… More >

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