Rough Deep Neural Architecture for Short-Term Wind Speed Forecasting

TLDR: Experimental results show that the proposed rough DNN models outperform classic DNNs and previous models that apply shallow architectures in the view of lower RMSE and mean absolute error measurements.

Abstract: Accurate wind speed forecasting is a fundamental requirement for large-scale integration of wind power generation. However, the intermittent and stochastic nature of wind speed makes this task challenging. Artificial neural networks (ANNs) are widely used in this area; however, they may fail to provide the accuracy that may be required. This is due to applying shallow architectures with error-prone hand-engineered features. This paper proposes a deep neural network (DNN) architecture with stacked autoencoder (SAE) and stacked denoising autoencoder (SDAE) for ultrashort-term and short-term wind speed forecasting. Autoencoders (AEs) are applied for unsupervised feature learning from the unlabeled wind data and a supervised regression layer is applied at the top of the AEs for wind speed forecasting. Several uncertain factors exist in the wind data that degrade the accuracy of current methodologies. In order to improve the accuracy, rough neural networks are incorporated in the proposed deep learning models to develop novel rough extensions of SAE and SDAE that are robust to wind uncertainties. Experimental results show that the proposed rough DNN models outperform classic DNNs and previous models that apply shallow architectures in the view of lower RMSE and mean absolute error measurements.