Quantitative Analysis of the Impact of Meteorological Environment on Photovoltaic System Feasibility
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 s...
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doaj-a45c03118432451bb7b445c048c1188c2021-06-01T00:17:34ZengMDPI AGEnergies1996-10732021-05-01142893289310.3390/en14102893Quantitative Analysis of the Impact of Meteorological Environment on Photovoltaic System FeasibilityDengchang Ma0Guobing Pan1Fang Xu2Hongfei Sun3The Institute of Distributed Energy and Microgrid, Zhejiang University of Technology, Hangzhou 310013, ChinaThe Institute of Distributed Energy and Microgrid, Zhejiang University of Technology, Hangzhou 310013, ChinaThe Institute of Distributed Energy and Microgrid, Zhejiang University of Technology, Hangzhou 310013, ChinaThe Institute of Distributed Energy and Microgrid, Zhejiang University of Technology, Hangzhou 310013, ChinaThe 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.https://www.mdpi.com/1996-1073/14/10/2893PV systemfeasibility studymeteorological environmentquantitative analysisRandom Forest Regression |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Dengchang Ma Guobing Pan Fang Xu Hongfei Sun |
spellingShingle |
Dengchang Ma Guobing Pan Fang Xu Hongfei Sun Quantitative Analysis of the Impact of Meteorological Environment on Photovoltaic System Feasibility Energies PV system feasibility study meteorological environment quantitative analysis Random Forest Regression |
author_facet |
Dengchang Ma Guobing Pan Fang Xu Hongfei Sun |
author_sort |
Dengchang Ma |
title |
Quantitative Analysis of the Impact of Meteorological Environment on Photovoltaic System Feasibility |
title_short |
Quantitative Analysis of the Impact of Meteorological Environment on Photovoltaic System Feasibility |
title_full |
Quantitative Analysis of the Impact of Meteorological Environment on Photovoltaic System Feasibility |
title_fullStr |
Quantitative Analysis of the Impact of Meteorological Environment on Photovoltaic System Feasibility |
title_full_unstemmed |
Quantitative Analysis of the Impact of Meteorological Environment on Photovoltaic System Feasibility |
title_sort |
quantitative analysis of the impact of meteorological environment on photovoltaic system feasibility |
publisher |
MDPI AG |
series |
Energies |
issn |
1996-1073 |
publishDate |
2021-05-01 |
description |
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. |
topic |
PV system feasibility study meteorological environment quantitative analysis Random Forest Regression |
url |
https://www.mdpi.com/1996-1073/14/10/2893 |
work_keys_str_mv |
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