Short-Term Multi-Step Ahead Wind Power Predictions Based On A Novel Deep Convolutional Recurrent Network Method
Xin Liu, Luoxiao Yang, Zijun Zhang
Abstract
In this paper, a novel deep convolutional recurrent network method, the K-shape and K-means guided Convolutional Neural Network integrating Gated Recurrent Units (KK-CNN-GRU), for short-term multi-step ahead predictions of wind turbine power generations is proposed. The KK-CNN-GRU is composed of three modules, an input tensor construction module, a two-layer clustering module, and a prediction module. In the input tensor construction module, data of spatial, temporal, and physical meaning features are fused and organized as a tensor. Meanwhile, the two-layer clustering module is used to recognize patterns of a wind power time series and offer a cluster membership to the associated input tensor. Finally, predictions are generated by feeding the input tensor into the prediction module, which is composed of a feature weighting unit, a convolutional neural network, and a set of gated recurrent unit cells with fully connected layers switched according to the cluster membership of the input tensor. In experimental studies, two sets of six-step ahead prediction experiments are conducted with considered prediction horizons from 7.1 seconds to 42.6 seconds and from 10 minutes to 1 hour, respectively. The high prediction accuracy of the proposed KK-CNN-GRU is validated by comparing with state-of-the-art benchmarking methods.