Wind speed forecasting using deep neural network with feature selection.

With the rapid growth of wind power penetration into modern power grids, wind speed forecasting (WSF) becomes an increasing important task in the planning and operation of electric power and energy systems. However, WSF is quite challengeable due to its highly varying and complex features. In this paper, a novel hybrid deep neural network forecasting method is constituted. A feature selection method based on mutual information is developed in the WSF problem. With the real-time big data from the wind farm running log, the deep neural network model for WSF is established using a stacked denoising auto-encoder and long short-term memory network. The effectiveness of the deep neural network is evaluated by 10-minutes-ahead WSF. Comparing with the traditional multi-layer perceptron network, conventional long short-term memory network and stacked auto-encoder, the resulting deep neural network significantly improves the forecasting accuracy. [ABSTRACT FROM AUTHOR]