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Journals / International Advanced Researches and Engineering Journal / 2026 / Volume: 10 Issue: 1

Probabilistic forecasting of short-term electricity prices in the Turkish day-ahead market

Kübra Nur Akpınar(Marmara Üniversitesi)0000-0003-4579-4070
Pages
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Publication Date
2026-04-20
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results show that the proposed approach provides high-performance prediction intervals when compared with traditional single-point models.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In modern power systems with increasing renewable energy integration, electricity price forecasting has become increasingly vital for system planning.
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Key Findings & Data:A Quantile Gradient Boosting Regressor (GBR) was trained using hourly data obtained from the EPİAŞ transparency platform covering the period between 2022 and 2025.
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Conclusion & Impact:The results show that the proposed approach provides high-performance prediction intervals when compared with traditional single-point models.
🔬Design:Modeling & Simulation
📍Scope:2022 Verileri
⚙️Variables:Day-Ahead Market · Gradient Boosting Regressor · Price Forecast

Abstract

In modern power systems with increasing renewable energy integration, electricity price forecasting has become increasingly vital for system planning. This study focuses on Türkiye’s Day-Ahead Market (DAM) prices by utilizing a probabilistic machine learning model to improve short-term price prediction. A Quantile Gradient Boosting Regressor (GBR) was trained using hourly data obtained from the EPİAŞ transparency platform covering the period between 2022 and 2025. By estimating market-clearing prices, the model allows for capturing both the central tendency and the uncertainty of prices. The model includes time stamp data as hour and day, as well as electricity generation resources and past prices. Quantitatively, the model achieved an RMSE of 434.82 TRY/MWh, a CRPS of 194.98 TRY/MWh, and a PICP of 0.74 for the 80% prediction interval. The results show that the proposed approach provides high-performance prediction intervals when compared with traditional single-point models. This probabilistic model could be used for decision-making in energy markets, as well as for the scheduling of renewable integrated storage systems within renewable energy systems.

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Cite This Article

Akpınar, K. N. (2026). Probabilistic forecasting of short-term electricity prices in the Turkish day-ahead market. International Advanced Researches and Engineering Journal, Volume: 10 Issue: 1. https://doi.org/10.35860/iarej.1820591

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