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Extensive comparison of physical models for photovoltaic power forecasting

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  • Mayer, Martin János
  • Gróf, Gyula

Abstract

Forecasting the power production of grid-connected photovoltaic (PV) power plants is essential for both the profitability and the prospects of the technology. Physically inspired modelling represents a common approach in calculating the expected power output from numerical weather prediction data. The model selection has a high effect on physical PV power forecasting accuracy, as the difference between the most and least accurate model chains is 13% in mean absolute error (MAE), 12% in root mean square error (RMSE), and 23–33% in skill scores for a PV plant on average. The power forecast performance analysis performed and verified for one-year 15-min resolution production data of 16 PV plants in Hungary for day-ahead and intraday time horizons on all possible combinations of nine direct and diffuse irradiance separation, ten tilted irradiance transposition, three reflection loss, five cell temperature, four PV module performance, two shading loss, and three inverter models.

Suggested Citation

  • Mayer, Martin János & Gróf, Gyula, 2021. "Extensive comparison of physical models for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:appene:v:283:y:2021:i:c:s0306261920316330
    DOI: 10.1016/j.apenergy.2020.116239
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    1. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    2. Sun, Xixi & Bright, Jamie M. & Gueymard, Christian A. & Acord, Brendan & Wang, Peng & Engerer, Nicholas A., 2019. "Worldwide performance assessment of 75 global clear-sky irradiance models using Principal Component Analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 111(C), pages 550-570.
    3. Quan, Hao & Yang, Dazhi, 2020. "Probabilistic solar irradiance transposition models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 125(C).
    4. Martin Hofmann & Gunther Seckmeyer, 2017. "Influence of Various Irradiance Models and Their Combination on Simulation Results of Photovoltaic Systems," Energies, MDPI, vol. 10(10), pages 1-24, September.
    5. Zepter, Jan Martin & Weibezahn, Jens, 2019. "Unit commitment under imperfect foresight – The impact of stochastic photovoltaic generation," Applied Energy, Elsevier, vol. 243(C), pages 336-349.
    6. Amaro e Silva, R. & Brito, M.C., 2019. "Spatio-temporal PV forecasting sensitivity to modules’ tilt and orientation," Applied Energy, Elsevier, vol. 255(C).
    7. Ana Maria Gracia Amillo & Thomas Huld & Paraskevi Vourlioti & Richard Müller & Matthew Norton, 2015. "Application of Satellite-Based Spectrally-Resolved Solar Radiation Data to PV Performance Studies," Energies, MDPI, vol. 8(5), pages 1-34, April.
    8. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    9. Ogliari, Emanuele & Dolara, Alberto & Manzolini, Giampaolo & Leva, Sonia, 2017. "Physical and hybrid methods comparison for the day ahead PV output power forecast," Renewable Energy, Elsevier, vol. 113(C), pages 11-21.
    10. Li, Yanting & He, Yong & Su, Yan & Shu, Lianjie, 2016. "Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines," Applied Energy, Elsevier, vol. 180(C), pages 392-401.
    11. Santiago, I. & Trillo-Montero, D. & Moreno-Garcia, I.M. & Pallarés-López, V. & Luna-Rodríguez, J.J., 2018. "Modeling of photovoltaic cell temperature losses: A review and a practice case in South Spain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 70-89.
    12. Theocharides, Spyros & Makrides, George & Livera, Andreas & Theristis, Marios & Kaimakis, Paris & Georghiou, George E., 2020. "Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing," Applied Energy, Elsevier, vol. 268(C).
    13. Castellano, Nuria Novas & Gázquez Parra, José Antonio & Valls-Guirado, Juan & Manzano-Agugliaro, Francisco, 2015. "Optimal displacement of photovoltaic array’s rows using a novel shading model," Applied Energy, Elsevier, vol. 144(C), pages 1-9.
    14. Yang, Dazhi & Wu, Elynn & Kleissl, Jan, 2019. "Operational solar forecasting for the real-time market," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1499-1519.
    15. Mayer, Martin János & Szilágyi, Artúr & Gróf, Gyula, 2020. "Environmental and economic multi-objective optimization of a household level hybrid renewable energy system by genetic algorithm," Applied Energy, Elsevier, vol. 269(C).
    16. Larson, David P. & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest," Renewable Energy, Elsevier, vol. 91(C), pages 11-20.
    17. Abreu, Edgar F.M. & Canhoto, Paulo & Costa, Maria João, 2019. "Prediction of diffuse horizontal irradiance using a new climate zone model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 28-42.
    18. Leva, S. & Dolara, A. & Grimaccia, F. & Mussetta, M. & Ogliari, E., 2017. "Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 131(C), pages 88-100.
    19. Starke, Allan R. & Lemos, Leonardo F.L. & Boland, John & Cardemil, José M. & Colle, Sergio, 2018. "Resolution of the cloud enhancement problem for one-minute diffuse radiation prediction," Renewable Energy, Elsevier, vol. 125(C), pages 472-484.
    20. de la Parra, I. & Muñoz, M. & Lorenzo, E. & García, M. & Marcos, J. & Martínez-Moreno, F., 2017. "PV performance modelling: A review in the light of quality assurance for large PV plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 780-797.
    21. Mattei, M. & Notton, G. & Cristofari, C. & Muselli, M. & Poggi, P., 2006. "Calculation of the polycrystalline PV module temperature using a simple method of energy balance," Renewable Energy, Elsevier, vol. 31(4), pages 553-567.
    22. Chin, Vun Jack & Salam, Zainal & Ishaque, Kashif, 2015. "Cell modelling and model parameters estimation techniques for photovoltaic simulator application: A review," Applied Energy, Elsevier, vol. 154(C), pages 500-519.
    23. Ridley, Barbara & Boland, John & Lauret, Philippe, 2010. "Modelling of diffuse solar fraction with multiple predictors," Renewable Energy, Elsevier, vol. 35(2), pages 478-483.
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