Solar Radiation Measurement Using Raspberry Pi and Its Modelling Using Artificial Neural Networks

Solar Radiation Measurement Using Raspberry Pi and Its Modelling Using Artificial Neural Networks

The advent of solar energy as the best alternative to traditional energy sources has led to an extensive study on the measurement and prediction of solar radiation. Devices such as pyranometer, pyrrheliometer, global UV radiometer are used for the measurement of solar radiation. The solar radiation...

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Main Authors: Priya Selvanathan Shanmuga, Freudenberg Norman Carl, Borkataky Arunabh
Format: Article
Language:English
Published: EDP Sciences 2016-01-01
Series:MATEC Web of Conferences
Online Access:http://dx.doi.org/10.1051/matecconf/20167706011
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spelling doaj-567c1a283d7a4c97a332631f3637de862021-02-02T04:22:17ZengEDP SciencesMATEC Web of Conferences2261-236X2016-01-01770601110.1051/matecconf/20167706011matecconf_icmmr2016_06011Solar Radiation Measurement Using Raspberry Pi and Its Modelling Using Artificial Neural NetworksPriya Selvanathan Shanmuga0Freudenberg Norman Carl1Borkataky Arunabh2Department of Chemical Engineering, Manipal Institute of Technology, Manipal UniversityDepartment of Physics, Carl von Ossietzky Universität OldenburgDepartment of Chemical Engineering, Manipal Institute of Technology, Manipal UniversityThe advent of solar energy as the best alternative to traditional energy sources has led to an extensive study on the measurement and prediction of solar radiation. Devices such as pyranometer, pyrrheliometer, global UV radiometer are used for the measurement of solar radiation. The solar radiation measuring instruments available at Innovation Center, MIT Manipal were integrated with a Raspberry Pi to allow remote access to the data through the university Local Area Network. The connections of the data loggers and the Raspberry Pi were enclosed in a plastic box to prevent damage from the rainfall and humidity in Manipal. The solar radiation data was used to validate an Artificial Neural Network model which was developed using various meterological data from 2011-2015.http://dx.doi.org/10.1051/matecconf/20167706011
collection DOAJ
language English
format Article
sources DOAJ
author Priya Selvanathan Shanmuga
Freudenberg Norman Carl
Borkataky Arunabh
spellingShingle Priya Selvanathan Shanmuga
Freudenberg Norman Carl
Borkataky Arunabh
Solar Radiation Measurement Using Raspberry Pi and Its Modelling Using Artificial Neural Networks
MATEC Web of Conferences
author_facet Priya Selvanathan Shanmuga
Freudenberg Norman Carl
Borkataky Arunabh
author_sort Priya Selvanathan Shanmuga
title Solar Radiation Measurement Using Raspberry Pi and Its Modelling Using Artificial Neural Networks
title_short Solar Radiation Measurement Using Raspberry Pi and Its Modelling Using Artificial Neural Networks
title_full Solar Radiation Measurement Using Raspberry Pi and Its Modelling Using Artificial Neural Networks
title_fullStr Solar Radiation Measurement Using Raspberry Pi and Its Modelling Using Artificial Neural Networks
title_full_unstemmed Solar Radiation Measurement Using Raspberry Pi and Its Modelling Using Artificial Neural Networks
title_sort solar radiation measurement using raspberry pi and its modelling using artificial neural networks
publisher EDP Sciences
series MATEC Web of Conferences
issn 2261-236X
publishDate 2016-01-01
description The advent of solar energy as the best alternative to traditional energy sources has led to an extensive study on the measurement and prediction of solar radiation. Devices such as pyranometer, pyrrheliometer, global UV radiometer are used for the measurement of solar radiation. The solar radiation measuring instruments available at Innovation Center, MIT Manipal were integrated with a Raspberry Pi to allow remote access to the data through the university Local Area Network. The connections of the data loggers and the Raspberry Pi were enclosed in a plastic box to prevent damage from the rainfall and humidity in Manipal. The solar radiation data was used to validate an Artificial Neural Network model which was developed using various meterological data from 2011-2015.
url http://dx.doi.org/10.1051/matecconf/20167706011
work_keys_str_mv AT priyaselvanathanshanmuga solarradiationmeasurementusingraspberrypianditsmodellingusingartificialneuralnetworks
AT freudenbergnormancarl solarradiationmeasurementusingraspberrypianditsmodellingusingartificialneuralnetworks
AT borkatakyarunabh solarradiationmeasurementusingraspberrypianditsmodellingusingartificialneuralnetworks
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