Deep Learning Model for Wind Forecasting: Classification Analyses for Temporal Meteorological Data

Abstract

AbstractThis paper proposes a multiple CNN architecture with multiple input features, combined with multiple LSTM, along with densely connected convolutional layers, for temporal wind nature analyses. The designed architecture is called Multiple features, Multiple Densely Connected Convolutional Neural Network with Multiple LSTM Architecture, i.e. MCLT. A total of 58 features in the input layers of the MCLT are designed using wind speed and direction values. These empirical features are based on percentage difference, standard deviation, correlation coefficient, eigenvalues, and entropy, for efficiently describing the wind trend. Two successive LSTM layers are used after four densely connected convolutional layers of the MCLT. Moreover, LSTM has memory units that utilise learnt features from the current as well as previous outputs of the neurons, thereby enhancing the learning of patterns in the temporal wind dataset. Densely connected convolutional layer helps to learn features of other convolutional layers as well. The MCLT is used to predict dominant speed and direction classes in the future for the wind datasets of Stuttgart and Netherlands. The maximum and minimum overall accuracies for dominant speed prediction are 99.1% and 94.9%, (for Stuttgart) and 99.9% and 97.5% (for Netherlands) and for dominant direction prediction are 99.9% and 94.4% (for Stuttgart) and 99.6% and 96.4% (for Netherlands), respectively, using MCLT with 58 features. The MCLT, therefore, with multiple features at different levels, i.e. the input layers, the convolutional layers, and LSTM layers, shows promising results for the prediction of dominant speed and direction. Thus, this work is useful for proper wind utilisation and improving environmental planning. These analyses would also help in performing Computational Fluid Dynamics (CFD) simulations using wind speed and direction measured at a nearby meteorological station, for devising a new set of appropriate inflow boundary conditions.