A diagnostic-guided CNN-BiLSTM-Transformer hybrid framework for reanalysis-based wind speed forecasting across climatic regions of India

Accurate short-term wind speed forecasting is critical for characterizing wind variability and supporting renewable energy planning, yet remains challenging due to the nonlinear, nonstationary, and region-dependent nature of atmospheric wind fields. This study presents a diagnostic-guided hybrid deep learning framework in which convolutional neural networks (CNN), bidirectional long short-term memory networks (BiLSTM), and Transformer-based attention mechanisms are systematically integrated according to diagnosed time-series characteristics to improve one-step-ahead hourly wind speed forecasting using reanalysis data. Architectural design choices are explicitly informed by autocorrelation structure, seasonal decomposition, and stationarity diagnostics, enabling a principled integration of local feature extraction, temporal dependency modeling, and long-range attention. A secondary, site-specific post hoc residual correction stage based on XGBoost is applied to mitigate systematic prediction biases in the deep learning outputs. The framework is evaluated using MERRA-2 reanalysis wind speed data spanning 2001-2024 across ten climatically diverse regions of India, employing a chronological train-validation-test split and multi-run evaluation using RMSE, MAE, MAPE, and \(R^2\) . The hybrid model consistently outperforms its individually trained CNN, BiLSTM, and Transformer components across most sites, achieving average RMSE reductions on the order of 14-16% relative to individual standalone architectures at selected sites with corresponding improvements in \(R^2\) , while residual correction yields additional but comparatively smaller gains. For contextual comparison, several transformer-based deep learning architectures inspired by recent large language model designs are evaluated under the same supervised training protocol, with less consistent performance observed for the considered numerical regression task. Limitations associated with reanalysis data and operational deployment are explicitly acknowledged.