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Prediction of I–V Characteristic Curve for Photovoltaic Modules Based on Convolutional Neural Network
Photovoltaic (PV) modules are exposed to the outside, which is affected by radiation, the temperature of the PV module back-surface, relative humidity, atmospheric pressure and other factors, which makes it difficult to test and analyze the performance of photovoltaic modules. Traditionally, the equ...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7180992/ https://www.ncbi.nlm.nih.gov/pubmed/32283723 http://dx.doi.org/10.3390/s20072119 |
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author | Li, Jie Li, Runran Jia, Yuanjie Zhang, Zhixin |
author_facet | Li, Jie Li, Runran Jia, Yuanjie Zhang, Zhixin |
author_sort | Li, Jie |
collection | PubMed |
description | Photovoltaic (PV) modules are exposed to the outside, which is affected by radiation, the temperature of the PV module back-surface, relative humidity, atmospheric pressure and other factors, which makes it difficult to test and analyze the performance of photovoltaic modules. Traditionally, the equivalent circuit method is used to analyze the performance of PV modules, but there are large errors. In this paper—based on machine learning methods and large amounts of photovoltaic test data—convolutional neural network (CNN) and multilayer perceptron (MLP) neural network models are established to predict the I–V curve of photovoltaic modules. Furthermore, the accuracy and the fitting degree of these methods for current–voltage (I–V) curve prediction are compared in detail. The results show that the prediction accuracy of the CNN and MLP neural network model is significantly better than that of the traditional equivalent circuit models. Compared with MLP models, the CNN model has better accuracy and fitting degree. In addition, the error distribution concentration of CNN has better robustness and the pre-test curve is smoother and has better nonlinear segment fitting effects. Thus, the CNN is superior to MLP model and the traditional equivalent circuit model in complex climate conditions. CNN is a high-confidence method to predict the performance of PV modules. |
format | Online Article Text |
id | pubmed-7180992 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-71809922020-04-30 Prediction of I–V Characteristic Curve for Photovoltaic Modules Based on Convolutional Neural Network Li, Jie Li, Runran Jia, Yuanjie Zhang, Zhixin Sensors (Basel) Article Photovoltaic (PV) modules are exposed to the outside, which is affected by radiation, the temperature of the PV module back-surface, relative humidity, atmospheric pressure and other factors, which makes it difficult to test and analyze the performance of photovoltaic modules. Traditionally, the equivalent circuit method is used to analyze the performance of PV modules, but there are large errors. In this paper—based on machine learning methods and large amounts of photovoltaic test data—convolutional neural network (CNN) and multilayer perceptron (MLP) neural network models are established to predict the I–V curve of photovoltaic modules. Furthermore, the accuracy and the fitting degree of these methods for current–voltage (I–V) curve prediction are compared in detail. The results show that the prediction accuracy of the CNN and MLP neural network model is significantly better than that of the traditional equivalent circuit models. Compared with MLP models, the CNN model has better accuracy and fitting degree. In addition, the error distribution concentration of CNN has better robustness and the pre-test curve is smoother and has better nonlinear segment fitting effects. Thus, the CNN is superior to MLP model and the traditional equivalent circuit model in complex climate conditions. CNN is a high-confidence method to predict the performance of PV modules. MDPI 2020-04-09 /pmc/articles/PMC7180992/ /pubmed/32283723 http://dx.doi.org/10.3390/s20072119 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, Jie Li, Runran Jia, Yuanjie Zhang, Zhixin Prediction of I–V Characteristic Curve for Photovoltaic Modules Based on Convolutional Neural Network |
title | Prediction of I–V Characteristic Curve for Photovoltaic Modules Based on Convolutional Neural Network |
title_full | Prediction of I–V Characteristic Curve for Photovoltaic Modules Based on Convolutional Neural Network |
title_fullStr | Prediction of I–V Characteristic Curve for Photovoltaic Modules Based on Convolutional Neural Network |
title_full_unstemmed | Prediction of I–V Characteristic Curve for Photovoltaic Modules Based on Convolutional Neural Network |
title_short | Prediction of I–V Characteristic Curve for Photovoltaic Modules Based on Convolutional Neural Network |
title_sort | prediction of i–v characteristic curve for photovoltaic modules based on convolutional neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7180992/ https://www.ncbi.nlm.nih.gov/pubmed/32283723 http://dx.doi.org/10.3390/s20072119 |
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