SCADA-Based AC Power Forecasting in a Utility-Scale PV Plant: Explainable HistGradientBoosting and Thermal Derating Quantification

Hasan Uzel

doi:10.54287/gujsa.1818552

SCADA-Based AC Power Forecasting in a Utility-Scale PV Plant: Explainable HistGradientBoosting and Thermal Derating Quantification

Abstract

Accurate photovoltaic (PV) power modeling is essential for grid operation, short-term scheduling and plant-level decision making. However, reported performance in the literature varies widely due to differences in feature selection, forecast horizon definition and evaluation protocols. In particular, true exogenous forecasting is often conflated with contemporaneous power tracking, leading to optimistic accuracy estimates with limited operational relevance. This study aims to clarify how feature availability, evaluation design and operational intent shape reported performance in PV power modeling. A leakage-aware and operationally realistic framework is proposed that explicitly distinguishes between exogenous forecasting and operational tracking tasks. Using high-frequency SCADA data from a utility-scale PV plant, two modeling scenarios are evaluated. Scenario A relies exclusively on exogenous meteorological inputs and represents a forecasting-oriented inference task, whereas Scenario B augments these inputs with synchronous electrical current measurements and is interpreted as operational tracking (nowcasting) rather than forward-looking prediction. Multiple regression models, with emphasis on tree-based ensemble methods, are evaluated under transparent and scenario-consistent train–test splitting strategies. Model behavior is examined using post-hoc explainability tools to assess physical consistency and temperature-induced power derating is quantified using a semiparametric regression approach. The results indicate that exogenous-only models recover physically meaningful relationships but exhibit higher numerical errors than current-augmented configurations, reflecting the informational advantage of synchronous measurements rather than improved forecasting capability. Rather than claiming superior numerical accuracy, this work demonstrates how modeling assumptions and evaluation choices directly influence reported performance and its interpretation. By aligning feature availability with operational intent, the proposed framework provides practical guidance for the development, evaluation and deployment of PV power models in real-world applications.

Keywords

Supporting Institution

No financial or institutional support was received for this study.

Ethical Statement

This study does not involve human participants, animals, or sensitive data, and therefore does not require ethical approval. The research was conducted using publicly available or institutionally collected SCADA data.

Thanks

The author gratefully acknowledges the support and encouragement of his family during the preparation of this manuscript.

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Details

Primary Language

English

Subjects

Deep Learning, Photovoltaic Power Systems

Journal Section

Research Article

Authors

Hasan Uzel ^*
0000-0002-8238-2588
Türkiye

Publication Date

March 31, 2026

Submission Date

November 6, 2025

Acceptance Date

February 17, 2026

Published in Issue

Year 2026 Volume: 13 Number: 1

DOI

https://doi.org/10.54287/gujsa.1818552

IZ

https://izlik.org/JA52DM97KL

Cite

RIS / Bibtex

APA

Uzel, H. (2026). SCADA-Based AC Power Forecasting in a Utility-Scale PV Plant: Explainable HistGradientBoosting and Thermal Derating Quantification. Gazi University Journal of Science Part A: Engineering and Innovation, 13(1), 452-482. https://doi.org/10.54287/gujsa.1818552

AMA

1.Uzel H. SCADA-Based AC Power Forecasting in a Utility-Scale PV Plant: Explainable HistGradientBoosting and Thermal Derating Quantification. GU J Sci, Part A. 2026;13(1):452-482. doi:10.54287/gujsa.1818552

Chicago

Uzel, Hasan. 2026. “SCADA-Based AC Power Forecasting in a Utility-Scale PV Plant: Explainable HistGradientBoosting and Thermal Derating Quantification”. Gazi University Journal of Science Part A: Engineering and Innovation 13 (1): 452-82. https://doi.org/10.54287/gujsa.1818552.

EndNote

Uzel H (March 1, 2026) SCADA-Based AC Power Forecasting in a Utility-Scale PV Plant: Explainable HistGradientBoosting and Thermal Derating Quantification. Gazi University Journal of Science Part A: Engineering and Innovation 13 1 452–482.

IEEE

[1]H. Uzel, “SCADA-Based AC Power Forecasting in a Utility-Scale PV Plant: Explainable HistGradientBoosting and Thermal Derating Quantification”, GU J Sci, Part A, vol. 13, no. 1, pp. 452–482, Mar. 2026, doi: 10.54287/gujsa.1818552.

ISNAD

Uzel, Hasan. “SCADA-Based AC Power Forecasting in a Utility-Scale PV Plant: Explainable HistGradientBoosting and Thermal Derating Quantification”. Gazi University Journal of Science Part A: Engineering and Innovation 13/1 (March 1, 2026): 452-482. https://doi.org/10.54287/gujsa.1818552.

JAMA

1.Uzel H. SCADA-Based AC Power Forecasting in a Utility-Scale PV Plant: Explainable HistGradientBoosting and Thermal Derating Quantification. GU J Sci, Part A. 2026;13:452–482.

MLA

Uzel, Hasan. “SCADA-Based AC Power Forecasting in a Utility-Scale PV Plant: Explainable HistGradientBoosting and Thermal Derating Quantification”. Gazi University Journal of Science Part A: Engineering and Innovation, vol. 13, no. 1, Mar. 2026, pp. 452-8, doi:10.54287/gujsa.1818552.

Vancouver

1.Hasan Uzel. SCADA-Based AC Power Forecasting in a Utility-Scale PV Plant: Explainable HistGradientBoosting and Thermal Derating Quantification. GU J Sci, Part A. 2026 Mar. 1;13(1):452-8. doi:10.54287/gujsa.1818552