The use of deep recurrent neural networks to predict performance of photovoltaic system for charging electric vehicles

Electric vehicles are fully ecological means of transport only when the electricity required to charge them comes from Renewable Energy Sources (RES). When building a photovoltaic carport, the complex of its functions must consider the power consumption necessary to charge an electric vehicle. The p...

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Autores principales:	Małek Arkadiusz, Marciniak Andrzej
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2021
Materias:	photovoltaic system electric vehicle deep recurrent neural networks machine learning numerical calculation applications Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/55edc42f99e8455d868ce815188629b9
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spelling	oai:doaj.org-article:55edc42f99e8455d868ce815188629b92021-12-05T14:10:46ZThe use of deep recurrent neural networks to predict performance of photovoltaic system for charging electric vehicles2391-543910.1515/eng-2021-0034https://doaj.org/article/55edc42f99e8455d868ce815188629b92021-02-01T00:00:00Zhttps://doi.org/10.1515/eng-2021-0034https://doaj.org/toc/2391-5439Electric vehicles are fully ecological means of transport only when the electricity required to charge them comes from Renewable Energy Sources (RES). When building a photovoltaic carport, the complex of its functions must consider the power consumption necessary to charge an electric vehicle. The performance of the photovoltaic system depends on the season and on the intensity of the sunlight, which in turn depends on the geographical conditions and the current weather. This means that even a large photovoltaic system is not always able to generate the amount of energy required to charge an electric vehicle. The problem discussed in the article is maximization of the share of renewable energy in the process of charging of electric vehicle batteries. Deep recurrent neural networks (RNN) trained on the past data collected by performance monitoring system can be applied to predict the future performance of the photovoltaic system. The accuracy of the presented forecast is sufficient to manage the process of the distribution of energy produced from renewable energy sources. The purpose of the numerical calculations is to maximize the use of the energy produced by the photovoltaic system for charging electric cars.Małek ArkadiuszMarciniak AndrzejDe Gruyterarticlephotovoltaic systemelectric vehicledeep recurrent neural networksmachine learningnumerical calculationapplicationsEngineering (General). Civil engineering (General)TA1-2040ENOpen Engineering, Vol 11, Iss 1, Pp 377-389 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	photovoltaic system electric vehicle deep recurrent neural networks machine learning numerical calculation applications Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	photovoltaic system electric vehicle deep recurrent neural networks machine learning numerical calculation applications Engineering (General). Civil engineering (General) TA1-2040 Małek Arkadiusz Marciniak Andrzej The use of deep recurrent neural networks to predict performance of photovoltaic system for charging electric vehicles
description	Electric vehicles are fully ecological means of transport only when the electricity required to charge them comes from Renewable Energy Sources (RES). When building a photovoltaic carport, the complex of its functions must consider the power consumption necessary to charge an electric vehicle. The performance of the photovoltaic system depends on the season and on the intensity of the sunlight, which in turn depends on the geographical conditions and the current weather. This means that even a large photovoltaic system is not always able to generate the amount of energy required to charge an electric vehicle. The problem discussed in the article is maximization of the share of renewable energy in the process of charging of electric vehicle batteries. Deep recurrent neural networks (RNN) trained on the past data collected by performance monitoring system can be applied to predict the future performance of the photovoltaic system. The accuracy of the presented forecast is sufficient to manage the process of the distribution of energy produced from renewable energy sources. The purpose of the numerical calculations is to maximize the use of the energy produced by the photovoltaic system for charging electric cars.
format	article
author	Małek Arkadiusz Marciniak Andrzej
author_facet	Małek Arkadiusz Marciniak Andrzej
author_sort	Małek Arkadiusz
title	The use of deep recurrent neural networks to predict performance of photovoltaic system for charging electric vehicles
title_short	The use of deep recurrent neural networks to predict performance of photovoltaic system for charging electric vehicles
title_full	The use of deep recurrent neural networks to predict performance of photovoltaic system for charging electric vehicles
title_fullStr	The use of deep recurrent neural networks to predict performance of photovoltaic system for charging electric vehicles
title_full_unstemmed	The use of deep recurrent neural networks to predict performance of photovoltaic system for charging electric vehicles
title_sort	use of deep recurrent neural networks to predict performance of photovoltaic system for charging electric vehicles
publisher	De Gruyter
publishDate	2021
url	https://doaj.org/article/55edc42f99e8455d868ce815188629b9
work_keys_str_mv	AT małekarkadiusz theuseofdeeprecurrentneuralnetworkstopredictperformanceofphotovoltaicsystemforchargingelectricvehicles AT marciniakandrzej theuseofdeeprecurrentneuralnetworkstopredictperformanceofphotovoltaicsystemforchargingelectricvehicles AT małekarkadiusz useofdeeprecurrentneuralnetworkstopredictperformanceofphotovoltaicsystemforchargingelectricvehicles AT marciniakandrzej useofdeeprecurrentneuralnetworkstopredictperformanceofphotovoltaicsystemforchargingelectricvehicles
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The use of deep recurrent neural networks to predict performance of photovoltaic system for charging electric vehicles

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