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Accurate wind power forecasting is of great significance for power system operation. In this study, a triple-stage multi-step wind power forecasting approach is proposed by applying attention-based deep residual gated recurrent unit (GRU) network combined with ensemble empirical mode decomposition (EEMD) and crisscross optimization algorithm (CSO). In the data processing stage, the EEMD is used to decompose the wind power/speed time series and a bi-attention mechanism (BA) is applied to enhance the sensitivity of model to the important information from both time and feature dimension. In the prediction stage, a series-connected deep learning model called RGRU consisting of the residual network and GRU is proposed as the forecasting model, aiming to make full use of extracting the static and dynamic coupling relationship among the input features. In the retraining-stage, a high-performance CSO algorithm is adopted to further optimize the fully-connected layer of RGRU model so as to improve the generalization ability of the model. The proposed method is validated on a wind farm located in Spain and the experimental results demonstrate that the proposed hybrid model has significant advantage over other state-of-the-art models involved in this study in terms of prediction accuracy and stability.

Although machine learning methods have been widely applied in the wind power prediction field, they are not suitable for building the prediction model of a new-built wind farm because of no sufficient historical data. In this study, a novel deep transfer learning approach is proposed for addressing the few-shot learning problem in multi-step ahead wind power prediction. In the pre-training stage, several convolutional neural networks (CNNs) in parallel are separately connected to the long short-term memory network (LSTM), thus forming a unique serio-parallel CNNs-LSTM (CL) feature extractor. The CL utilizes the CNNs and LSTM to extract both the meteorological and temporal feature information of the neighboring wind farms for facilitating the prediction modeling of the source wind farm. In the transfer-training stage, a transfer strategy is designed to transfer partial network parameters of a well-trained CL feature extractor to construct the prediction model of the target wind farm. In addition, a personalized-training strategy is implemented by using crisscross optimization (CSO) to retrain the parameters of fully-connected layer. The proposed method is validated on a swarm of wind farms located in China and the experimental results show its obvious superiority over the non-transfer models involved in this study.

Wind power forecasting is crucial for the economic dispatch and operation of power system. In this study, a novel hybrid wind power prediction approach is proposed by applying a cascaded deep learning model to extract the implicit meteorological and temporal characteristics of each subseries generated by a two-layer of mode decomposition method. In the proposed model, the empirical mode decomposition is employed to decompose the original time series into a set of intrinsic mode functions (IMFs) and the variational mode decomposition is applied to further decompose the IMF1 sub-layers into several sub-series because of the irregular feature of IMF1. To make use of the coupling relationship between wind power sub-layer, wind speed sub-layer and wind direction, convolutional neural network is used to extract the implicit features of these relationship and then long short-term memory utilizes these features as inputs and further extract the temporal correlation hidden features in each time sub-series. The eventual predicted results are obtained by superimposing the predicted values of all subsequences. The experimental results illustrate that: (a) The prediction performance is obviously improved when the proposed two-layer of decomposition is considered. (b) To achieve better prediction accuracy, it is proven to be an effective way to apply convolutional neural network and long short-term memory to extract the implicit meteorological relationship and the temporal correlation characteristic hidden in each decomposed time sub-series, respectively. (c) The proposed hybrid model outperforms other hybrid models involved in this study and shows a promising prospect in the short-term wind power prediction.