Day-Ahead Solar Power Forecasting Using a Hybrid Model Combining Regression and Physical Model Chain

Authors

  • Erwin Pauang Pongmasakke Universitas Indonesia, National Taiwan University of Science and Technology, PT. Perusahaan Listrik Negara
  • Jian-Hong Liu National Taiwan University of Science and Technology
  • Budi Sudiarto Universitas Indonesia

DOI:

https://doi.org/10.62146/ijecbe.v3i1.108

Keywords:

Hybrid method, NWP, Physical model chain, Day-ahead PV power forecast, XGBoost

Abstract

Solar power forecasting is essential for integrating PV plants into power grids, ensuring stability and aiding system operators (SOs) in decision-making. However, existing day-ahead models struggle with rapid weather changes, while deep learning models require extensive historical data, making them impractical for new PV plants.
This study proposes a hybrid approach combining the XGBoost algorithm for hourly solar irradiance prediction using Numerical Weather Prediction (NWP) data and a physical model to convert irradiance into power. The XGBoost model is periodically retrained via a sliding window mechanism to adapt to dynamic weather conditions.
A case study using two years of 271 kWp PV data from NIST (US) and historical NWP data from ECMWF ENS for GHI forecasting, alongside ECMWF HRES for power conversion, demonstrated the method’s effectiveness. Using just one week of historical data for initial training, the model achieved an nRMSE of 13.35%–13.53%, nMAE of 6.9%–7.03%, and nMBE of -2.03% to -0.29%.

The proposed approach improves PV forecasting reliability for new plants with limited data, serving as an intermediary solution until sufficient historical data is available for deep learning models.

Author Biographies

Erwin Pauang Pongmasakke, Universitas Indonesia, National Taiwan University of Science and Technology, PT. Perusahaan Listrik Negara

Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok, Indonesia

Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Republic of China (Taiwan)

PT. Perusahaan Listrik Negara, PLN (Persero)

Jian-Hong Liu, National Taiwan University of Science and Technology

Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Republic of China (Taiwan)

Budi Sudiarto, Universitas Indonesia

Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok, Indonesia

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Published

2025-05-21

How to Cite

Pongmasakke, E. P., Liu, J.-H., & Sudiarto, B. (2025). Day-Ahead Solar Power Forecasting Using a Hybrid Model Combining Regression and Physical Model Chain. International Journal of Electrical, Computer, and Biomedical Engineering, 3(1), 91–116. https://doi.org/10.62146/ijecbe.v3i1.108

Issue

Section

Electrical and Electronics Engineering

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