Open Access

ARTICLE

Day-Ahead Electricity Price Forecasting Using the XGBoost Algorithm: An Application to the Turkish Electricity Market

Yağmur Yılan, Ahad Beykent^*

Department of Industrial Engineering, Faculty of Engineering, Ondokuz Mayis University, Samsun, 55139, Türkiye

* Corresponding Author: Ahad Beykent. Email:

(This article belongs to the Special Issue: Advancements and Challenges in Artificial Intelligence, Data Analysis and Big Data)

Computers, Materials & Continua 2026, 86(1), 1-16. https://doi.org/10.32604/cmc.2025.068440

Received 29 May 2025; Accepted 15 September 2025; Issue published 10 November 2025

Abstract

Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies, hedge risk and plan generation schedules. By leveraging advanced data analytics and machine learning methods, accurate and reliable price forecasts can be achieved. This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting (XGBoost). We benchmark XGBoost against four alternatives—Support Vector Machines (SVM), Long Short-Term Memory (LSTM), Random Forest (RF), and Gradient Boosting (GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul (EXIST). All models were trained on an identical chronological 80/20 train–test split, with hyperparameters tuned via 5-fold cross-validation on the training set. XGBoost achieved the best performance (Mean Absolute Error (MAE) = 144.8 TRY/MWh, Root Mean Square Error (RMSE) = 201.8 TRY/MWh, coefficient of determination (R²) = 0.923) while training in 94 s. To enhance interpretability and identify key drivers, we employed Shapley Additive Explanations (SHAP), which highlighted a strong association between higher prices and increased natural-gas-based generation. The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.

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Keywords

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