A novel deep learning approach for short-term wind power forecasting based on infinite feature selection and recurrent neural network

There are many features that have been taken into consideration for wind power forecasting. Since properly ranking these relevant features, often redundant, can be quite difficult, highly accurate short term wind power forcasting remains a big challenge. Another noted problem that adversely impacts the accuracy of wind forcasting stems from the weakness of the prevailing prediction models based on the feedforward neural network (FNN) in handling wind power time series. This paper thus attempts to address the aforementioned problems in short-term wind power forecasting with a novel approach that combines the infinite feature selection (Inf-FS) with the recurrent neural networks (RNN). In particular, all the possible features related to wind forecast are first clustered into multiple feature sets, after which the identified feature sets are mapped onto the paths of a graph built for Inf-FS. Traversing such a graph helps effectively determine/rank the significance of the features according to their stability and classification accuracy measured in the feature space. The proposed wind prediction model then feeds the ranked features into a deep learning prediction system enabled by RNN, whose neurons have self-feedback loops to help gather the past decisions, and thus be more effective than FNN for wind power prediction. The proposed wind power prediction approach is demonstrated through the experimental evaluations using a dataset from the National Renewable Energy Laboratory (NREL). The result shows that the accuracy of short-term wind power forecast is increased by 11%, 29%, 33%, and 19% in spring, summer, autumn and winter, respectively, over that achieved using the traditional approaches.