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Machine learning for site-adaptation and solar radiation forecasting

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  • Narvaez, Gabriel
  • Giraldo, Luis Felipe
  • Bressan, Michael
  • Pantoja, Andres

Abstract

Optimal management for solar energy systems requires quality data to build accurate models for predicting the behavior of solar radiation. Solar irradiance and environmental data are provided by satellite and in-situ measurements. It is usual that satellite measurements present high temporal resolution with limited spatial resolution, and in-situ measurements provide high accuracy but significant missing data. This paper proposes a methodology based on machine learning algorithms that: i) takes the best of both data sources to obtain an improved spatio-temporal resolution, known as site-adaptation; and ii) provides highly accurate forecasting solar-radiation models based on deep learning on the improved data. Through a study case with real data, we show the benefits of using the proposed methodology based on machine and deep learning techniques to integrate data from different sources and to construct precise solar radiation forecasting models in regions where solar energy systems are required. Results show that machine learning models for site-adaptation performed up to 38% better than traditional methods.

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  • Narvaez, Gabriel & Giraldo, Luis Felipe & Bressan, Michael & Pantoja, Andres, 2021. "Machine learning for site-adaptation and solar radiation forecasting," Renewable Energy, Elsevier, vol. 167(C), pages 333-342.
    • Handle: RePEc:eee:renene:v:167:y:2021:i:c:p:333-342
    DOI: 10.1016/j.renene.2020.11.089
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    3. Qin, Jun & Jiang, Hou & Lu, Ning & Yao, Ling & Zhou, Chenghu, 2022. "Enhancing solar PV output forecast by integrating ground and satellite observations with deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
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    5. Han, Jen-Yu & Vohnicky, Petr, 2022. "An optimized approach for mapping solar irradiance in a mid-low latitude region based on a site-adaptation technique using Himawari-8 satellite imageries," Renewable Energy, Elsevier, vol. 187(C), pages 603-617.
    6. Akhter, Muhammad Naveed & Mekhilef, Saad & Mokhlis, Hazlie & Ali, Raza & Usama, Muhammad & Muhammad, Munir Azam & Khairuddin, Anis Salwa Mohd, 2022. "A hybrid deep learning method for an hour ahead power output forecasting of three different photovoltaic systems," Applied Energy, Elsevier, vol. 307(C).
    7. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    8. Cheng, Lilin & Zang, Haixiang & Wei, Zhinong & Zhang, Fengchun & Sun, Guoqiang, 2022. "Evaluation of opaque deep-learning solar power forecast models towards power-grid applications," Renewable Energy, Elsevier, vol. 198(C), pages 960-972.
    9. Muhammad Naveed Akhter & Saad Mekhilef & Hazlie Mokhlis & Ziyad M. Almohaimeed & Munir Azam Muhammad & Anis Salwa Mohd Khairuddin & Rizwan Akram & Muhammad Majid Hussain, 2022. "An Hour-Ahead PV Power Forecasting Method Based on an RNN-LSTM Model for Three Different PV Plants," Energies, MDPI, vol. 15(6), pages 1-21, March.
    10. Christos Kyriakos & Manolis Vavalis, 2023. "Business Intelligence through Machine Learning from Satellite Remote Sensing Data," Future Internet, MDPI, vol. 15(11), pages 1-29, October.
    11. Zang, Haixiang & Jiang, Xin & Cheng, LiLin & Zhang, Fengchun & Wei, Zhinong & Sun, Guoqiang, 2022. "Combined empirical and machine learning modeling method for estimation of daily global solar radiation for general meteorological observation stations," Renewable Energy, Elsevier, vol. 195(C), pages 795-808.
    12. Jiang, Hou & Lu, Ning & Yao, Ling & Qin, Jun & Liu, Tang, 2023. "Impact of climate changes on the stability of solar energy: Evidence from observations and reanalysis," Renewable Energy, Elsevier, vol. 208(C), pages 726-736.
    13. Hongchao Zhang & Tengteng Zhu, 2022. "Stacking Model for Photovoltaic-Power-Generation Prediction," Sustainability, MDPI, vol. 14(9), pages 1-16, May.
    14. Salamalikis, Vasileios & Tzoumanikas, Panayiotis & Argiriou, Athanassios A. & Kazantzidis, Andreas, 2022. "Site adaptation of global horizontal irradiance from the Copernicus Atmospheric Monitoring Service for radiation using supervised machine learning techniques," Renewable Energy, Elsevier, vol. 195(C), pages 92-106.
    15. Wang, Zhijin & Liu, Xiufeng & Huang, Yaohui & Zhang, Peisong & Fu, Yonggang, 2023. "A multivariate time series graph neural network for district heat load forecasting," Energy, Elsevier, vol. 278(PA).

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