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Quantitative Analysis of the Impact of Meteorological Environment on Photovoltaic System Feasibility

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  • Dengchang Ma

    (The Institute of Distributed Energy and Microgrid, Zhejiang University of Technology, Hangzhou 310013, China
    The College of Electrical Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China)

  • Guobing Pan

    (The Institute of Distributed Energy and Microgrid, Zhejiang University of Technology, Hangzhou 310013, China)

  • Fang Xu

    (The Institute of Distributed Energy and Microgrid, Zhejiang University of Technology, Hangzhou 310013, China)

  • Hongfei Sun

    (The Institute of Distributed Energy and Microgrid, Zhejiang University of Technology, Hangzhou 310013, China)

Abstract

The meteorological environment is a determining factor in photovoltaic (PV) system feasibility (PVSF). To evaluate this impact more accurately, a quantitative analysis model based on multimeteorological factors and the Random Forest Regression model is proposed in this work. Firstly, an evaluation system is established to assess the impact. Then, to predict the indicators of the evaluation system, a parameter, i.e., performance ratio in sampling period, is defined. Secondly, a set of essential influences on the performance ratio in the sampling period is established through analyzing and reducing the discovered influences on the PV system performance. Finally, data from the Desert Knowledge Australia Solar Centre (DKASC) website are used to conduct the experiment. During the experiment, the sample set is cleaned using the model based on the cosine of the zenith angle. The functional relationship between the performance ratio in the sampling period and its essential influences is established through training a Random Forest Regression model with the data of the modeling system. The data of the test system are used to verify the forecast performance of the proposed model. Compared with the reference model, which is based on the traditional physical experiment, the results of the proposed model accord better with the measured values.

Suggested Citation

  • Dengchang Ma & Guobing Pan & Fang Xu & Hongfei Sun, 2021. "Quantitative Analysis of the Impact of Meteorological Environment on Photovoltaic System Feasibility," Energies, MDPI, vol. 14(10), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:10:p:2893-:d:556393
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    2. Alfredo Nespoli & Andrea Matteri & Silvia Pretto & Luca De Ciechi & Emanuele Ogliari, 2021. "Battery Sizing for Different Loads and RES Production Scenarios through Unsupervised Clustering Methods," Forecasting, MDPI, vol. 3(4), pages 1-19, September.

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