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One dimensional convolutional neural network architectures for wind prediction

Harbola, Shubhi, Coors, Volker
Energy conversion and management 2019 v.195 pp. 70-75
algorithms, data collection, neural networks, prediction, wind power, wind speed, wind turbines, Germany, Netherlands
This paper proposes two one-dimensional (1D) convolutional neural networks (CNNs) for predicting dominant wind speed and direction for the temporal wind dataset. The proposed 1D Single CNN (1DS) takes as input the consecutive temporal values in terms of the wind speed and direction and predicts in future dominating speed and direction, separately, after the last value in the input. The developed 1D Multiple CNN (1DM) combines several 1DS but with different views of the same input, therefore, learning more information compared to the 1DS. The proposed algorithms have been trained and tested using the historical wind datasets of Stuttgart (Germany) and Netherlands for different months. Total accuracies reached up to 95.2%, 95.1% for predicting the dominant wind speed and direction, respectively, using the 1DS and up to 98.8%, 99.7% for predicting the dominant wind speed and direction, respectively, using the 1DM. Unlike other methods which use regression techniques with manually designed features for predicting speed and direction, the proposed methods have used classification techniques with the 1DS and 1DM learning their features automatically on original wind dataset. Further, predicted dominant speed and direction in this work would be helpful in wind turbine installation whose power output depends on above parameters.