Wind Cast AI : A Comparative Analysis of Machine Learning Model for Short Term Wind Power Forecasting

This paper presented WindCast AI, a deployable Flask-based wind power forecasting system that trains and compares six model families — Random Forest, XGBoost, LSTM, GRU, Transformer, and a Proposed Hybrid Transformer-BiLSTM-Attention model — under an identical evaluation protocol, and extends the comparison with a real-time 24– 48 hour medium-term forecasting module built on live atmospheric data. On the evaluated dataset, XGBoost achieved the best RMSE while the Proposed Hybrid model achieved the best MAPE, underscoring that architecture selection should be guided by the error characteristic most relevant to the deployment context rather than a single aggregate metric.

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