INTEGRATING ECONOMETRIC AND DEEP LEARNING MODELS FOR ENERGY PRICE PREDICTION: A HYBRID APPROACH USING WEATHER AND MARKET DATA

Authors : Cemal Öztürk

Pages : 135-175

Doi:10.55071/ticaretfbd.1578209

View : 61 | Download : 68

Publication Date : 2025-06-30

Article Type : Research Paper

Abstract :This study proposes a hybrid approach that integrates econometric and deep learning models—specifically, Vector Autoregression (VAR), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)—to enhance electricity price forecasting. By combining historical data with external factors like weather and market indicators, this hybrid approach aims to improve prediction accuracy in volatile energy markets. The model captures complex temporal dependencies through a hybrid VAR, LSTM, and GRU structure and is tested on historical electricity price data supplemented with weather and market variables. Performance is evaluated using mean absolute error (MAE), root mean square error (RMSE), symmetric mean absolute percentage error (SMAPE), and root mean squared logarithmic error (RMSLE). Results show that deep learning models, particularly GRU, outperform VAR regarding MAE, RMSE, and RMSLE, suggesting superior predictive accuracy for absolute and relative forecasting tasks. However, SMAPE results highlight that the VAR model performs better in capturing proportional errors, suggesting its relative robustness in volatile price environments. Including weather and market data significantly improves the model’s robustness and accuracy. This study’s hybrid approach combines the interpretability of econometric models with the predictive power of deep learning, offering insights into the impact of external factors on energy prices. The model supports better decision-making and risk management for energy market participants in dynamic market environments.
Keywords : Enerji Fiyat Tahmini, Geçitli Tekrarlayan Birim (GRU), Uzun Kısa Süreli Bellek (LSTM), Derin Öğrenme Modelleri, Zaman Serisi Analizi

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