Modeling solar potential in Semarang, Indonesia using artificial neural networks

Artificial neural network shows a good performance in predicting renewable energy. Many versions of Artificial Neural Network (ANN) models have been implemented to predict solar potential. This study aims to determine the monthly solar radiation in Semarang, Indonesia using ANN, and to visualize mon...

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Autores principales:	Widodo Djoko Adi, Purwanto Purwanto, Hermawan Hermawan
Formato:	article
Lenguaje:	EN
Publicado:	Institut za istrazivanja i projektovanja u privredi 2021
Materias:	artificial neural network solar irradiance solar energy potential back propagation Technology T Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/cc66b0d022174047a620b5cf6c841ba3
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spelling	oai:doaj.org-article:cc66b0d022174047a620b5cf6c841ba32021-12-05T21:23:12ZModeling solar potential in Semarang, Indonesia using artificial neural networks1451-41171821-319710.5937/jaes0-29025https://doaj.org/article/cc66b0d022174047a620b5cf6c841ba32021-01-01T00:00:00Zhttps://scindeks-clanci.ceon.rs/data/pdf/1451-4117/2021/1451-41172103578W.pdfhttps://doaj.org/toc/1451-4117https://doaj.org/toc/1821-3197Artificial neural network shows a good performance in predicting renewable energy. Many versions of Artificial Neural Network (ANN) models have been implemented to predict solar potential. This study aims to determine the monthly solar radiation in Semarang, Indonesia using ANN, and to visualize monthly solar irradiance as a map of the solar system of Semarang. This research applied the perceptron multi-layer ANN model, with 7 variables as input data of network learning, which were maximum temperature, relative humidity, wind speed, rainfall, longitude, latitude, and elevation. The input data set was obtained from a NASA normalized geo-satellite database website with a 5-year average daily score. Network training used backpropagation with one of the input layers, two of hidden layers, and one of the output layer. The performance of the model during the analysis of mean absolute percentage error was highly accurate (6.6%) when 12 and 10 neurons were respectively installed in the first and second hidden layers. The result was presented in a monthly map of solar potential within the geographical information system (GIS) environment. The result showed that ANN was able to be one of the alternatives to estimate solar irradiance data. The sun irradiance map can be used by the government of Semarang City to provide information about the solar energy profile for the implementation of the solar energy system.Widodo Djoko AdiPurwanto PurwantoHermawan HermawanInstitut za istrazivanja i projektovanja u privrediarticleartificial neural networksolar irradiancesolar energy potentialback propagationTechnologyTEngineering (General). Civil engineering (General)TA1-2040ENIstrazivanja i projektovanja za privredu, Vol 19, Iss 3, Pp 578-585 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial neural network solar irradiance solar energy potential back propagation Technology T Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	artificial neural network solar irradiance solar energy potential back propagation Technology T Engineering (General). Civil engineering (General) TA1-2040 Widodo Djoko Adi Purwanto Purwanto Hermawan Hermawan Modeling solar potential in Semarang, Indonesia using artificial neural networks
description	Artificial neural network shows a good performance in predicting renewable energy. Many versions of Artificial Neural Network (ANN) models have been implemented to predict solar potential. This study aims to determine the monthly solar radiation in Semarang, Indonesia using ANN, and to visualize monthly solar irradiance as a map of the solar system of Semarang. This research applied the perceptron multi-layer ANN model, with 7 variables as input data of network learning, which were maximum temperature, relative humidity, wind speed, rainfall, longitude, latitude, and elevation. The input data set was obtained from a NASA normalized geo-satellite database website with a 5-year average daily score. Network training used backpropagation with one of the input layers, two of hidden layers, and one of the output layer. The performance of the model during the analysis of mean absolute percentage error was highly accurate (6.6%) when 12 and 10 neurons were respectively installed in the first and second hidden layers. The result was presented in a monthly map of solar potential within the geographical information system (GIS) environment. The result showed that ANN was able to be one of the alternatives to estimate solar irradiance data. The sun irradiance map can be used by the government of Semarang City to provide information about the solar energy profile for the implementation of the solar energy system.
format	article
author	Widodo Djoko Adi Purwanto Purwanto Hermawan Hermawan
author_facet	Widodo Djoko Adi Purwanto Purwanto Hermawan Hermawan
author_sort	Widodo Djoko Adi
title	Modeling solar potential in Semarang, Indonesia using artificial neural networks
title_short	Modeling solar potential in Semarang, Indonesia using artificial neural networks
title_full	Modeling solar potential in Semarang, Indonesia using artificial neural networks
title_fullStr	Modeling solar potential in Semarang, Indonesia using artificial neural networks
title_full_unstemmed	Modeling solar potential in Semarang, Indonesia using artificial neural networks
title_sort	modeling solar potential in semarang, indonesia using artificial neural networks
publisher	Institut za istrazivanja i projektovanja u privredi
publishDate	2021
url	https://doaj.org/article/cc66b0d022174047a620b5cf6c841ba3
work_keys_str_mv	AT widododjokoadi modelingsolarpotentialinsemarangindonesiausingartificialneuralnetworks AT purwantopurwanto modelingsolarpotentialinsemarangindonesiausingartificialneuralnetworks AT hermawanhermawan modelingsolarpotentialinsemarangindonesiausingartificialneuralnetworks
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Modeling solar potential in Semarang, Indonesia using artificial neural networks

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