A two-channel deep network based model for improving ultra-short-term prediction of wind power via utilizing multi-source data

Data-driven predictions of wind turbine power outputs have received numerous discussions with using turbine data from the supervisory control and data acquisition (SCADA) system itself. It is of a high curiosity on studying the feasibility of extending the prediction capability with additionally considering data of other sources and the data-driven modeling principle of doing so. To respond such problem and introduce more innovative prediction techniques, this paper presents a pioneering attempt of studying a two-channel deep network modeling method for wind power predictions which leverage both the wind farm data and farm geoinformation. Novelty can be profiled as follows: 1) To accommodate multi-source data in the input, a new high-dimensional input form of two components, a tensor and a graph, is developed; 2) To advance predictions with such input, a deep graph attention convolutional recurrent (GACR) method, which develops one novel deep network channel with stacking multiple graph convolution and long short term memory (GCN-LSTM) layers for engineering high-level latent features from high-dimensional inputs and another classical feature selection channel for directly engaging valuable wind turbine attributes, is proposed. Comprehensive computational experiments are conducted to verify the value of such modeling development by comparing it with a set of competitive benchmarking models. An ablation study is also conducted to explain the necessity and value of network modules in the proposed method. A new state-of-the-art prediction performance is achieved.