Hybrid Deep Learning Models With Attention Mechanisms For Short-Term Wind Speed Forecasting: A Comprehensive Review And Benchmarking Of CNN–Bilstm–Attention Using Synthetic SCADA-Style Data For Robust Preliminary Validation
DOI:
https://doi.org/10.63665/IJMEC.1105.01Keywords:
Wind Speed Forecasting, CNN–BiLSTM–Attention, Hybrid Deep Learning, Attention Mechanism, Synthetic SCADA Data, Preliminary Validation, Renewable Energy IntegrationAbstract
The integration of wind energy into modern power systems demands forecasting frameworks capable of handling the non-linear, non-stationary, and chaotic behaviour of wind dynamics. While machine learning and deep learning approaches have individually demonstrated substantial gains over classical statistical methods, hybrid architectures coupling convolutional feature extraction with bidirectional recurrent memory and attention mechanisms have recently emerged as the most promising paradigm for short-term wind speed prediction. This paper presents a comprehensive review of hybrid deep learning models with attention mechanisms and introduces a preliminary benchmarking of a CNN–BiLSTM–Attention architecture using a synthetic SCADA-style hourly dataset. The synthetic dataset was generated to replicate the statistical properties of real onshore wind turbine telemetry, including diurnal cycles, seasonal variability, and stochastic fluctuations characteristic of Indian wind corridors. Six forecasting models were benchmarked: ARIMA, Support Vector Regression (SVR), Random Forest, XG Boost, Long Short-Term Memory (LSTM), and the proposed CNN–BiLSTM–Attention hybrid. Using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R²) as evaluation metrics, the hybrid model achieved the lowest errors (MAE = 0.83 m/s, RMSE = 1.15 m/s, MAPE = 13.8%, R² = 0.923) and demonstrated robust behaviour across low-, medium-, and high-wind regimes. The controlled synthetic environment provides a reproducible baseline for assessing architectural stability before deployment on field data. Findings confirm that the CNN–BiLSTM–Attention architecture delivers consistent improvements over both classical and standalone deep learning baselines, and establish a structured protocol for preliminary validation of hybrid forecasting models prior to real-SCADA deployment.
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