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Power-Weighted Prediction of Photovoltaic Power Generation in the Context of Structural Equation Modeling

Author

Listed:
  • Hongbo Zhu

    (School of Geomatics, Liaoning Technical University, Fuxin 123000, China)

  • Bing Zhang

    (School of Geomatics, Liaoning Technical University, Fuxin 123000, China
    Collaborative Innovation Institute of Geospatial Information Service, Liaoning Technical University, Fuxin 123000, China)

  • Weidong Song

    (School of Geomatics, Liaoning Technical University, Fuxin 123000, China
    Collaborative Innovation Institute of Geospatial Information Service, Liaoning Technical University, Fuxin 123000, China)

  • Jiguang Dai

    (School of Geomatics, Liaoning Technical University, Fuxin 123000, China
    Collaborative Innovation Institute of Geospatial Information Service, Liaoning Technical University, Fuxin 123000, China)

  • Xinmei Lan

    (School of Geomatics, Liaoning Technical University, Fuxin 123000, China)

  • Xinyue Chang

    (School of Geomatics, Liaoning Technical University, Fuxin 123000, China)

Abstract

With the popularization of solar energy development and utilization, photovoltaic power generation is widely used in countries around the world and is increasingly becoming an important part of new energy generation. However, it cannot be ignored that changes in solar radiation and meteorological conditions can cause volatility and intermittency in power generation, which, in turn, affects the stability and security of the power grid. Therefore, many studies aim to solve this problem by constructing accurate power prediction models for PV plants. However, most studies focus on adjusting the photovoltaic power station prediction model structure and parameters to achieve a high prediction accuracy. Few studies have examined how the various parameters affect the output of photovoltaic power plants, as well as how significantly and effectively these elements influence the forecast accuracy. In this study, we evaluate the correlations between solar irradiance intensity (GHI), atmospheric density (ρ), cloudiness (CC), wind speed (WS), relative humidity (RH), and ambient temperature (T) and a photovoltaic power station using a Pearson correlation analysis and remove the factors that have little correlation. The direct and indirect effects of the five factors other than wind speed (CC) on the photovoltaic power station are then estimated based on structural equation modeling; the indirect effects are generated by the interaction between the variables and ultimately have an impact on the power of the photovoltaic power station. Particle swarm optimization-based support vector regression (PSO-SVR) and variable weights utilizing the Mahalanobis distance were used to estimate the power of the photovoltaic power station over a short period of time, based on the contribution of the various solar radiation and climatic elements. Experiments were conducted on the basis of the measured data from a distributed photovoltaic power station in Changzhou, Jiangsu province, China. The results demonstrate that the short-term power of a photovoltaic power station is significantly influenced by the global horizontal irradiance (GHI), ambient temperature (T), and atmospheric density (ρ). Furthermore, the results also demonstrate how calculating the relative importance of the various contributing factors can help to improve the accuracy when estimating how powerful a photovoltaic power station will be. The multiple weighted regression model described in this study is demonstrated to be superior to the standard multiple regression model (PSO-SVR). The multiple weighted regression model resulted in a 7.2% increase in R 2 , a 10.7% decrease in the sum of squared error (SSE), a 2.2% decrease in the root mean square error (RMSE), and a 2.06% decrease in the continuous ranked probability score (CRPS).

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:10808-:d:1190609
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    References listed on IDEAS

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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. 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. Giovanni Brusco & Alessandro Burgio & Daniele Menniti & Anna Pinnarelli & Nicola Sorrentino & Pasquale Vizza, 2017. "Quantification of Forecast Error Costs of Photovoltaic Prosumers in Italy," Energies, MDPI, vol. 10(11), pages 1-17, November.
    4. 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.
    5. Heo, Jae & Song, Kwonsik & Han, SangUk & Lee, Dong-Eun, 2021. "Multi-channel convolutional neural network for integration of meteorological and geographical features in solar power forecasting," Applied Energy, Elsevier, vol. 295(C).
    6. Abbas Mardani & Dalia Streimikiene & Edmundas Kazimieras Zavadskas & Fausto Cavallaro & Mehrbakhsh Nilashi & Ahmad Jusoh & Habib Zare, 2017. "Application of Structural Equation Modeling (SEM) to Solve Environmental Sustainability Problems: A Comprehensive Review and Meta-Analysis," Sustainability, MDPI, vol. 9(10), pages 1-65, October.
    7. Hamidreza Ghazvinian & Sayed-Farhad Mousavi & Hojat Karami & Saeed Farzin & Mohammad Ehteram & Md Shabbir Hossain & Chow Ming Fai & Huzaifa Bin Hashim & Vijay P Singh & Faizah Che Ros & Ali Najah Ahme, 2019. "Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-24, May.
    8. 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.
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