Volume 6 • Issue 1 • PP: 55–71 • 2026
A Comparative Deep Learning Approach for Short-Term Wind Power Generation Prediction
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Accurate wind power forecasting is essential for reliable renewable energy integration, grid stability, reserve scheduling, and wind farm operation because turbine output is highly variable and strongly influenced by meteorological conditions. However, forecasting wind power remains challenging due to the nonlinear relationship between weather variables and power generation, the temporal dependency of hourly observations, and the circular nature of wind direction data. This study aims to develop and compare deep learning models for predicting normalized wind turbine power output using a field-based hourly dataset collected from an operational wind energy site starting from January 2, 2017. The dataset includes temperature, relative humidity, dew point, wind speed at 10 m and 100 m, wind direction at 10 m and 100 m, wind gusts, and normalized turbine output. Five predictive models, namely LSTM, RNN, GRU, CNN, and Dense neural networks, were trained and evaluated after applying data preprocessing procedures, including missing-value handling, feature scaling, temporal alignment, and wind-direction transformation. Model performance was assessed using MSE, RMSE, MAE, MBE, correlation coefficient (r), coefficient of determination (R2), RRMSE, NSE, and WI. The empirical results showed that recurrent architectures outperformed the CNN and Dense models, confirming the importance of temporal learning in hourly wind power forecasting. Among all models, LSTM achieved the best overall performance, with MSE = 0.0008, RMSE = 0.0282, MAE = 0.0106, MBE = -0.0006, r = 0.9940, R2 = 0.9880, RRMSE = 0.0861, NSE = 0.9880, and WI = 0.9970. These findings demonstrate that LSTM can effectively capture nonlinear and sequential relationships between meteorological variables and turbine power generation, providing a reliable forecasting approach for operational wind energy management and supporting more stable integration of wind power into modern electricity systems.
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