KaydedilenlerDerginizi ekleyin

Dergiler / Gazi University Journal of Science Part A: Engineering and Innovation / 2026 / Volume: 13 Issue: 1

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

Hasan Uzel(AMASYA UNIVERSITY, FACULTY OF ARTS AND SCIENCES)0000-0002-8238-2588
Sayfa
—
Yayın Tarihi
2026-03-31
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Accurate photovoltaic (PV) power modeling is essential for grid operation, short-term scheduling and plant-level decision making.
📊
Temel Bulgular & Veri:However, reported performance in the literature varies widely due to differences in feature selection, forecast horizon definition and evaluation protocols.
💡
Sonuç & Katkı: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.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Photovoltaic (PV) · SCADA · AC Power Forecasting

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.

🔖

Bu Makaleye Atıf Yap

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, Volume: 13 Issue: 1. https://doi.org/10.54287/gujsa.1818552

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (29)

  1. Alcañiz, A., Grzebyk, D., Ziar, H., & Isabella, O. (2023). Trends and gaps in photovoltaic power forecasting with machine learning. Energy Reports, 9, 447–471. https://doi.org/10.1016/J.EGYR.2022.11.208
  2. Antonanzas, J., Osorio, N., Escobar, R., Urraca, R., Martinez-de-Pison, F. J., & Antonanzas-Torres, F. (2016). Review of photovoltaic power forecasting. Solar Energy, 136, 78–111. https://doi.org/10.1016/J.SOLENER.2016.06.069
  3. Barhmi, K., Heynen, C., Golroodbari, S., & van Sark, W. (2024). A Review of Solar Forecasting Techniques and the Role of Artificial Intelligence. Solar 2024, Vol. 4, Pages 99-135, 4(1), 99–135. https://doi.org/10.3390/SOLAR4010005
  4. Bergmeir, C., Hyndman, R. J., & Koo, B. (2018). A note on the validity of cross-validation for evaluating autoregressive time series prediction. Computational Statistics & Data Analysis, 120, 70–83. https://doi.org/10.1016/J.CSDA.2017.11.003
  5. Böök, H., & Lindfors, A. V. (2020). Site-specific adjustment of a NWP-based photovoltaic production forecast. Solar Energy, 211, 779–788. https://doi.org/10.1016/J.SOLENER.2020.10.024
  6. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017). Classification and regression trees. Classification and Regression Trees, 1–358. https://doi.org/10.1201/9781315139470
  7. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17-Augu, 785–794. https://doi.org/10.1145/2939672.2939785
  8. Di Leo, P., Ciocia, A., Malgaroli, G., & Spertino, F. (2025). Advancements and Challenges in Photovoltaic Power Forecasting: A Comprehensive Review. Energies 2025, Vol. 18, Page 2108, 18(8), 2108. https://doi.org/10.3390/EN18082108
  9. Draper, N. R., & Smith, H. (2014). Applied regression analysis. In Applied Regression Analysis. wiley. https://doi.org/10.1002/9781118625590
  10. Dunn, P. K., & Smyth, G. K. (2005). Series evaluation of Tweedie exponential dispersion model densities. Statistics and Computing 2005 15:4, 15(4), 267–280. https://doi.org/10.1007/S11222-005-4070-Y
  11. Eseye, A. T., Zhang, J., & Zheng, D. (2018). Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information. Renewable Energy, 118, 357–367. https://doi.org/10.1016/J.RENENE.2017.11.011
  12. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/AOS/1013203451
  13. Fu, J., Sun, Y., Li, Y., Wang, W., Wei, W., Ren, J., Han, S., & Di, H. (2025). An investigation of photovoltaic power forecasting in buildings considering shadow effects: Modeling approach and SHAP analysis. Renewable Energy, 245, 122821. https://doi.org/10.1016/J.RENENE.2025.122821
  14. Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688. https://doi.org/10.1016/J.IJFORECAST.2006.03.001
  15. Inman, R. H., Pedro, H. T. C., & Coimbra, C. F. M. (2013). Solar forecasting methods for renewable energy integration. Progress in Energy and Combustion Science, 39(6), 535–576. https://doi.org/10.1016/J.PECS.2013.06.002
  16. Kanagolkar, A. (n.d.). SolarGeneration. Retrieved September 30, 2025, from https://www.kaggle.com/datasets/arunkanagolkar/solargeneration/data
  17. Kuzlu, M., Cali, U., Sharma, V., & Güler, Ö. (2020). Gaining insight into solar photovoltaic power generation forecasting utilizing explainable artificial intelligence tools. IEEE Access, 8, 187814–187823. https://doi.org/10.1109/ACCESS.2020.3031477
  18. Lundberg, S. M., & Lee, S. I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems, 2017-December, 4766–4775. https://arxiv.org/pdf/1705.07874
  19. Nan, C., Hao, Y., Huang, X., Wang, H., & Yang, H. (2024). Investigation on temperature dependence of recent high-efficiency silicon solar modules. Solar Energy Materials and Solar Cells, 266, 112649. https://doi.org/10.1016/J.SOLMAT.2023.112649
  20. Perin Gasparin, F., Detzel Kipper, F., Schuck de Oliveira, F., & Krenzinger, A. (2022). Assessment on the variation of temperature coefficients of photovoltaic modules with solar irradiance. Solar Energy, 244, 126–133. https://doi.org/10.1016/J.SOLENER.2022.08.052
  21. Piantadosi, G., Dutto, S., Galli, A., De Vito, S., Sansone, C., & Di Francia, G. (2024). Photovoltaic power forecasting: A Transformer based framework. Energy and AI, 18, 100444. https://doi.org/10.1016/J.EGYAI.2024.100444
  22. Porawagamage, G., Dharmapala, K., Chaves, J. S., Villegas, D., & Rajapakse, A. (2024). A review of machine learning applications in power system protection and emergency control: opportunities, challenges, and future directions. Frontiers in Smart Grids, 3, 1371153. https://doi.org/10.3389/frsgr.2024.1371153
  23. Singla, P., Duhan, M., & Saroha, S. (2021). A comprehensive review and analysis of solar forecasting techniques. Frontiers in Energy 2021 16:2, 16(2), 187–223. https://doi.org/10.1007/S11708-021-0722-7
  24. Skoplaki, E., & Palyvos, J. A. (2009). On the temperature dependence of photovoltaic module electrical performance: A review of efficiency/power correlations. Solar Energy, 83(5), 614–624. https://doi.org/10.1016/J.SOLENER.2008.10.008
  25. Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing 2004 14:3, 14(3), 199–222. https://doi.org/10.1023/B:STCO.0000035301.49549.88
  26. Sobri, S., Koohi-Kamali, S., & Rahim, N. A. (2018). Solar photovoltaic generation forecasting methods: A review. Energy Conversion and Management, 156, 459–497. https://doi.org/10.1016/J.ENCONMAN.2017.11.019
  27. Wang, J., Li, P., Ran, R., Che, Y., & Zhou, Y. (2018). A Short-Term Photovoltaic Power Prediction Model Based on the Gradient Boost Decision Tree. Applied Sciences 2018, Vol. 8, Page 689, 8(5), 689. https://doi.org/10.3390/APP8050689
  28. Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79–82. https://doi.org/10.3354/CR030079
  29. Xie, R., Pan, G., Liang, C., Lin, B., & Yu, O. (2025). Research on Output Prediction Method of Large-Scale Photovoltaic Power Station Based on Gradient-Boosting Decision Trees. Processes 2025, Vol. 13, Page 477, 13(2), 477. https://doi.org/10.3390/PR13020477