A novel time-frequency recurrent network and its advanced version for short-term wind speed predictions

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For the sensible and efficient use of wind energy, accurate wind speed forecast is crucial. To improve the accuracy of short-term wind speed prediction, a novel recurrent neural network known as the time-frequency recurrent neural network, or TFR for short, is developed. The wavelet transformation is naturally incorporated into the TFR architecture in order to mine the time-frequency characteristics. Additionally, the convolution processes are combined to extract the inherent correlation of time series, enhancing the TFR's performance and creating an advanced model known as CNN-TFR. The prediction ability, parameter sensitivity, and training time of the suggested models for multi-step wind speed forecasts are examined using the wealth of wind speed data from a genuine observation site. It is found that TFR offers greater prediction performance as compared to conventional recurrent neural networks since it can access frequency domain knowledge. Additionally, CNN-TFR's prediction performance has been further improved, making it superior to other CNN based models. For the proposed CNN-TFR model, its sensitivity to input length and wavelet parameters is investigated. It has been shown that with little training time, the CNN-TFR model with strong and robust prediction ability can be utilized to anticipate real wind speed.