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A Comprehensive Case Study of Wind Energy Production Forecasting in Türkiye Using Enhanced Attention BiLSTM
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DOI：10.1002/we.70114.png)
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Abstract
En 中文
The global demand for electrical energy continues to rise steadily, with T & uuml;rkiye experiencing particularly significant growth in energy consumption. To address this increasing demand sustainably, renewable energy sources (RES) have become the primary focus, with wind energy (WE) leading the transition. This study presents a comprehensive case study of wind energy production forecasting for a specific region in T & uuml;rkiye, utilizing advanced machine learning (ML) methodologies. The study employs long short-term memory (LSTM) and enhanced attention bidirectional long short-term memory (EABiLSTM) models to predict wind energy production using real-time generation data and comprehensive meteorological parameters. The methodology encompasses rigorous data preprocessing techniques, hyperparameter optimization, including normalization, temporal feature engineering, and advanced validation strategies. A comprehensive hourly dataset forms the foundation of this analysis, providing robust temporal coverage for training and validation. Meteorological data are sourced from the NASA Power project, while wind power plant production data are obtained from the Energy Markets Operation Corporation of T & uuml;rkiye (EPIAS) transparency platform, ensuring reliable generation records. The performance evaluation employs multiple metrics, including mean absolute error (MAE), coefficient of determination (), and root mean square error (RMSE), to assess forecasting accuracy. The prediction accuracy and reliability of the proposed EABiLSTM method were validated through temporal stability tests, seasonal robustness evaluation, and uncertainty quantification analysis. The effectiveness of the proposed methodologies is demonstrated through comparative analysis between standard LSTM and EABiLSTM models. The regional case study approach provides practical insights for wind energy operators and grid planners, contributing to renewable energy optimization strategies and supporting the country's sustainable energy transition goals.
Keywords:
attention
BiLSTM
forecasting
LSTM
machine learning
renewable energy
wind energy
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