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Inter-Hour Forecast of Solar Radiation Based on the Structural Equation Model and Ensemble Model

Author

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  • Tingting Zhu

    (College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
    Key Laboratory of Measurement and Control of Complex Systems of Engineering (Southeast University), Ministry of Education, Nanjing 210096, China
    The two authors contributed equally to this work.)

  • Yiren Guo

    (College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
    The two authors contributed equally to this work.)

  • Cong Wang

    (College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Chao Ni

    (College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China)

Abstract

Given the wide applications of photovoltaic (PV) power generation, the volatility in generation caused by solar radiation, which limits the capacity of the power grid, cannot be ignored. Therefore, much research has aimed to address this issue through the development of methods for accurately predicting inter-hour solar radiation and then estimating PV power. However, most forecasting methods focus on adjusting the model structure or model parameters to achieve prediction accuracy. There is little research discussing how different factors influence solar radiation and, thereby, the effectiveness of these data-driven methods regarding their prediction accuracy. In this work, the effects of several potential factors on solar radiation are estimated using correlation analysis and a structural equation model; an ensemble model is developed for predicting inter-hour solar radiation based on the interaction of those key factors. Several experiments are carried out based on an open database provided by the National Renewable Energy Laboratory. The results show that solar zenith angle, cloud cover, aerosols, and airmass have great effects on solar radiation. It is also shown that the selection of the key factor is more important than the model structure construction for predicting solar radiation precisely. The proposed ensemble model proves to outperform all sub-models and achieves about a 12% improvement over the persistent model based on the normalized root mean squared error statistic.

Suggested Citation

  • Tingting Zhu & Yiren Guo & Cong Wang & Chao Ni, 2020. "Inter-Hour Forecast of Solar Radiation Based on the Structural Equation Model and Ensemble Model," Energies, MDPI, vol. 13(17), pages 1-15, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4534-:d:407463
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    References listed on IDEAS

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    1. Kihan Kim & Jin Hur, 2019. "Weighting Factor Selection of the Ensemble Model for Improving Forecast Accuracy of Photovoltaic Generating Resources," Energies, MDPI, vol. 12(17), pages 1-13, August.
    2. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    3. Kambezidis, H.D. & Psiloglou, B.E. & Karagiannis, D. & Dumka, U.C. & Kaskaoutis, D.G., 2016. "Recent improvements of the Meteorological Radiation Model for solar irradiance estimates under all-sky conditions," Renewable Energy, Elsevier, vol. 93(C), pages 142-158.
    4. AlSkaif, Tarek & Dev, Soumyabrata & Visser, Lennard & Hossari, Murhaf & van Sark, Wilfried, 2020. "A systematic analysis of meteorological variables for PV output power estimation," Renewable Energy, Elsevier, vol. 153(C), pages 12-22.
    5. Muhammad Aslam & Jae-Myeong Lee & Hyung-Seung Kim & Seung-Jae Lee & Sugwon Hong, 2019. "Deep Learning Models for Long-Term Solar Radiation Forecasting Considering Microgrid Installation: A Comparative Study," Energies, MDPI, vol. 13(1), pages 1-15, December.
    6. Benali, L. & Notton, G. & Fouilloy, A. & Voyant, C. & Dizene, R., 2019. "Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components," Renewable Energy, Elsevier, vol. 132(C), pages 871-884.
    7. Zhenyu Wang & Cuixia Tian & Qibing Zhu & Min Huang, 2018. "Hourly Solar Radiation Forecasting Using a Volterra-Least Squares Support Vector Machine Model Combined with Signal Decomposition," Energies, MDPI, vol. 11(1), pages 1-21, January.
    8. John Boland, 2020. "Characterising Seasonality of Solar Radiation and Solar Farm Output," Energies, MDPI, vol. 13(2), pages 1-15, January.
    9. Zhu, Tingting & Wei, Haikun & Zhao, Xin & Zhang, Chi & Zhang, Kanjian, 2017. "Clear-sky model for wavelet forecast of direct normal irradiance," Renewable Energy, Elsevier, vol. 104(C), pages 1-8.
    10. Evermann, Joerg & Tate, Mary, 2016. "Assessing the predictive performance of structural equation model estimators," Journal of Business Research, Elsevier, vol. 69(10), pages 4565-4582.
    11. Kambezidis, H.D. & Psiloglou, B.E. & Karagiannis, D. & Dumka, U.C. & Kaskaoutis, D.G., 2017. "Meteorological Radiation Model (MRM v6.1): Improvements in diffuse radiation estimates and a new approach for implementation of cloud products," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 616-637.
    12. Roberto Langella & Daniela Proto & Alfredo Testa, 2016. "Solar Radiation Forecasting, Accounting for Daily Variability," Energies, MDPI, vol. 9(3), pages 1-17, March.
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    Cited by:

    1. Aleksander Radovan & Viktor Šunde & Danijel Kučak & Željko Ban, 2021. "Solar Irradiance Forecast Based on Cloud Movement Prediction," Energies, MDPI, vol. 14(13), pages 1-25, June.
    2. Tingting Zhu & Yuanzhe Li & Zhenye Li & Yiren Guo & Chao Ni, 2022. "Inter-Hour Forecast of Solar Radiation Based on Long Short-Term Memory with Attention Mechanism and Genetic Algorithm," Energies, MDPI, vol. 15(3), pages 1-14, January.
    3. Hongbo Zhu & Bing Zhang & Weidong Song & Jiguang Dai & Xinmei Lan & Xinyue Chang, 2023. "Power-Weighted Prediction of Photovoltaic Power Generation in the Context of Structural Equation Modeling," Sustainability, MDPI, vol. 15(14), pages 1-18, July.

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