Wind Speed Prediction using Deep Ensemble Learning with a Jet-like Architecture
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Accurate and reliable prediction of wind speed is a challenging task, because it depends on meteorological features of the surrounding region. In this work a novel Deep Ensemble Learning using Jet-like Architecture (DEL-Jet) approach is proposed. The proposed (DEL-Jet) technique is tested on wind speed prediction problem. As wind speed data is of the time series nature, so two Convolutional Neural Networks (CNNs) in addition to a deep Auto-Encoder (AE) are used to extract the feature space from input data. Whereas, Non-linear Principal Component Analysis (NLPCA) is employed to further reduce the dimensionality of extracted feature space. Finally, reduced feature space along with original feature space are used to train the meta-regressor for forecasting final wind speed. To show the effectiveness of work, performance of the proposed DEL-Jet technique is evaluated for ten independent runs and compared against commonly used regressors.
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