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Evaluating the Potential of Gaussian Process Regression for Solar Radiation Forecasting: A Case Study

Author

Listed:
  • Foster Lubbe

    (Department of Mechanical and Mechatronic Engineering, Stellenbosch University, Stellenbosch 7602, South Africa
    The first author is currently affiliated with the Centre for Surface Chemistry and Catalysis, KU Leuven, Celestijnenlaan 200F, 3001 Leuven, Belgium.)

  • Jacques Maritz

    (Department of Engineering Sciences, University of the Free State, Bloemfontein 9301, South Africa)

  • Thomas Harms

    (Department of Mechanical and Mechatronic Engineering, Stellenbosch University, Stellenbosch 7602, South Africa)

Abstract
The proliferation of solar power systems could cause instability within existing power grids due to the variable nature of solar power. A well-defined statistical model is important for managing the supply-and-demand dynamics of a power system that contains a significant variable renewable energy component. It is furthermore important to consider the inherent uncertainty in the data when modeling such a complex power system. Gaussian process regression has the potential to address both of these concerns: the probabilistic modeling of solar radiation data could assist in managing the variability of solar power, as well as provide a mechanism to deal with uncertainty. In this paper, solar radiation data was obtained from the Southern African Universities Radiometric Network and used to train a Gaussian process regression model which was developed especially for this purpose. Attention was given to constructing an appropriate Gaussian process kernel. It was found that a carefully constructed kernel allowed for the successful interpolation of global horizontal irradiance data, with a root-mean-squared error of 82.2W/m 2 . Gaps in the data, due to possible meter failure, were also bridged by the Gaussian process with a root-mean-squared error of 94.1 W/m 2 and accompanying confidence intervals. A root-mean-squared error of 151.1 W/m 2 was found when forecasting the global horizontal irradiance with a forecasting horizon of five days. These results, achieved in modeling solar radiation data using Gaussian process regression, could open new avenues in the development of probabilistic renewable energy management systems. Such systems could aid smart grid operators and support energy trading platforms, by allowing for better-informed decisions that incorporate the inherent uncertainty of stochastic power systems.

Suggested Citation

  • Foster Lubbe & Jacques Maritz & Thomas Harms, 2020. "Evaluating the Potential of Gaussian Process Regression for Solar Radiation Forecasting: A Case Study," Energies, MDPI, vol. 13(20), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:20:p:5509-:d:432082
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    References listed on IDEAS

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    1. Sun, Li & Jin, Yuhui & You, Fengqi, 2020. "Active disturbance rejection temperature control of open-cathode proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 261(C).
    2. Jacques Maritz & Foster Lubbe & Louis Lagrange, 2018. "A Practical Guide to Gaussian Process Regression for Energy Measurement and Verification within the Bayesian Framework," Energies, MDPI, vol. 11(4), pages 1-12, April.
    3. Yang, Yandong & Li, Shufang & Li, Wenqi & Qu, Meijun, 2018. "Power load probability density forecasting using Gaussian process quantile regression," Applied Energy, Elsevier, vol. 213(C), pages 499-509.
    4. Hanany Tolba & Nouha Dkhili & Julien Nou & Julien Eynard & Stéphane Thil & Stéphane Grieu, 2020. "Multi-Horizon Forecasting of Global Horizontal Irradiance Using Online Gaussian Process Regression: A Kernel Study," Energies, MDPI, vol. 13(16), pages 1-23, August.
    5. Herman Carstens & Xiaohua Xia & Sarma Yadavalli, 2018. "Bayesian Energy Measurement and Verification Analysis," Energies, MDPI, vol. 11(2), pages 1-20, February.
    6. A. Bumpus & S. Comello, 2017. "Emerging clean energy technology investment trends," Nature Climate Change, Nature, vol. 7(6), pages 382-385, June.
    7. Anand Krishnan Prakash & Susu Xu & Ram Rajagopal & Hae Young Noh, 2018. "Robust Building Energy Load Forecasting Using Physically-Based Kernel Models," Energies, MDPI, vol. 11(4), pages 1-21, April.
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    Cited by:

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    5. Ajith, Meenu & Martínez-Ramón, Manel, 2023. "Deep learning algorithms for very short term solar irradiance forecasting: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).

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