Better wind forecasting using Evolutionary Neural Architecture search driven Green Deep Learning

[Display omitted] •Time-series Analysis and Decomposition of wind time-series data.•Evolutionary Neural Architecture Search inspired by Green Deep Learning.•Comprehensive Comparison of optimal LSTMs, optimal NAR, and optimal WNNs.•Usage of real wind characteristics data to validate the proposed algorithm.•The significance of long-term variability for accurate forecasting of wind is studied. Climate Change heavily impacts global cities, the downsides of which can be minimized by adopting renewables like wind energy. However, despite its advantages, the nonlinear nature of wind renders the forecasting approaches to design and control wind farms ineffective. To expand the research horizon, the current study a) analyses and performs statistical decomposition of real-world wind time-series data, b) presents the application of Long Short-Term Memory (LSTM) networks, Nonlinear Auto-Regressive (NAR) models, and Wavelet Neural Networks (WNN) as efficient models for accurate wind forecasting with a comprehensive comparison among them to justify their application and c) proposes an evolutionary multi-objective strategy for Neural Architecture Search (NAS) to minimize the computational cost associated with training and inferring the networks which form the central theme of Green Deep Learning.Balancing the trade-off between parsimony and prediction accuracy, the proposed NAS strategy could optimally design NAR, WNN, and LSTM models with a mean test accuracy of 99%. The robust methodologies discussed in this work not only accurately model the wind behavior but also provide a green & generic approach for designing Deep Neural Networks.