Measurement of Global Solar Radiation data using Raspberry Pi and its estimation using Genetic Algorithm

The demand for more efficient and environmentally benign, non-conventional sources of energy came into picture due to increasing demands for human comforts. Solar energy is now the ultimate option. In this paper, the instruments used to measure the solar radiation at Innovation Centre, MIT Manipal w...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Priya S.Shanmuga [verfasserIn] Borkataky Arunabh [verfasserIn] Reddy Sneha [verfasserIn] Thirunavukkarasu I. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch ; Französisch

Erschienen:	2018

Übergeordnetes Werk:	In: MATEC Web of Conferences - EDP Sciences, 2013, 153, p 07004(2018)
Übergeordnetes Werk:	volume:153, p 07004 ; year:2018

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1051/matecconf/201815307004

Katalog-ID:	DOAJ048367664

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spelling	10.1051/matecconf/201815307004 doi (DE-627)DOAJ048367664 (DE-599)DOAJ3694815e8fd744d1a421f8774d18ca5e DE-627 ger DE-627 rakwb eng fre TA1-2040 Priya S.Shanmuga verfasserin aut Measurement of Global Solar Radiation data using Raspberry Pi and its estimation using Genetic Algorithm 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The demand for more efficient and environmentally benign, non-conventional sources of energy came into picture due to increasing demands for human comforts. Solar energy is now the ultimate option. In this paper, the instruments used to measure the solar radiation at Innovation Centre, MIT Manipal were connected to a Raspberry Pi to access the data remotely. Genetic Algorithms were formulated, so that the monthly mean global solar radiation in Manipal can be effectively estimated. Meteorological data such as humidity, temperature, wind speed, etc. were used as inputs to train the networks. A successful network was made between the data loggers and the Raspberry Pi. The data collected by the data loggers from the devices are transmitted to the Raspberry Pi which in turn sends the data to an internal server. The Raspberry Pi can be accessed using any SSH client such as PuTTY. The meteorological data was collected for the years 2010-2014 in order to formulate the Artificial Intelligence models. The validity of the formulated models were checked by comparing the measured data with the estimated data using tools such as RMSE, correlation coefficient, etc. The modelling of solar radiation using GA was carried out in GeneXpro tools version 5.0. Engineering (General). Civil engineering (General) Borkataky Arunabh verfasserin aut Reddy Sneha verfasserin aut Thirunavukkarasu I. verfasserin aut In MATEC Web of Conferences EDP Sciences, 2013 153, p 07004(2018) (DE-627)720166209 (DE-600)2673602-0 2261236X nnns volume:153, p 07004 year:2018 https://doi.org/10.1051/matecconf/201815307004 kostenfrei https://doaj.org/article/3694815e8fd744d1a421f8774d18ca5e kostenfrei https://doi.org/10.1051/matecconf/201815307004 kostenfrei https://doaj.org/toc/2261-236X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 153, p 07004 2018
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allfieldsGer	10.1051/matecconf/201815307004 doi (DE-627)DOAJ048367664 (DE-599)DOAJ3694815e8fd744d1a421f8774d18ca5e DE-627 ger DE-627 rakwb eng fre TA1-2040 Priya S.Shanmuga verfasserin aut Measurement of Global Solar Radiation data using Raspberry Pi and its estimation using Genetic Algorithm 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The demand for more efficient and environmentally benign, non-conventional sources of energy came into picture due to increasing demands for human comforts. Solar energy is now the ultimate option. In this paper, the instruments used to measure the solar radiation at Innovation Centre, MIT Manipal were connected to a Raspberry Pi to access the data remotely. Genetic Algorithms were formulated, so that the monthly mean global solar radiation in Manipal can be effectively estimated. Meteorological data such as humidity, temperature, wind speed, etc. were used as inputs to train the networks. A successful network was made between the data loggers and the Raspberry Pi. The data collected by the data loggers from the devices are transmitted to the Raspberry Pi which in turn sends the data to an internal server. The Raspberry Pi can be accessed using any SSH client such as PuTTY. The meteorological data was collected for the years 2010-2014 in order to formulate the Artificial Intelligence models. The validity of the formulated models were checked by comparing the measured data with the estimated data using tools such as RMSE, correlation coefficient, etc. The modelling of solar radiation using GA was carried out in GeneXpro tools version 5.0. Engineering (General). Civil engineering (General) Borkataky Arunabh verfasserin aut Reddy Sneha verfasserin aut Thirunavukkarasu I. verfasserin aut In MATEC Web of Conferences EDP Sciences, 2013 153, p 07004(2018) (DE-627)720166209 (DE-600)2673602-0 2261236X nnns volume:153, p 07004 year:2018 https://doi.org/10.1051/matecconf/201815307004 kostenfrei https://doaj.org/article/3694815e8fd744d1a421f8774d18ca5e kostenfrei https://doi.org/10.1051/matecconf/201815307004 kostenfrei https://doaj.org/toc/2261-236X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 153, p 07004 2018
allfieldsSound	10.1051/matecconf/201815307004 doi (DE-627)DOAJ048367664 (DE-599)DOAJ3694815e8fd744d1a421f8774d18ca5e DE-627 ger DE-627 rakwb eng fre TA1-2040 Priya S.Shanmuga verfasserin aut Measurement of Global Solar Radiation data using Raspberry Pi and its estimation using Genetic Algorithm 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The demand for more efficient and environmentally benign, non-conventional sources of energy came into picture due to increasing demands for human comforts. Solar energy is now the ultimate option. In this paper, the instruments used to measure the solar radiation at Innovation Centre, MIT Manipal were connected to a Raspberry Pi to access the data remotely. Genetic Algorithms were formulated, so that the monthly mean global solar radiation in Manipal can be effectively estimated. Meteorological data such as humidity, temperature, wind speed, etc. were used as inputs to train the networks. A successful network was made between the data loggers and the Raspberry Pi. The data collected by the data loggers from the devices are transmitted to the Raspberry Pi which in turn sends the data to an internal server. The Raspberry Pi can be accessed using any SSH client such as PuTTY. The meteorological data was collected for the years 2010-2014 in order to formulate the Artificial Intelligence models. The validity of the formulated models were checked by comparing the measured data with the estimated data using tools such as RMSE, correlation coefficient, etc. The modelling of solar radiation using GA was carried out in GeneXpro tools version 5.0. Engineering (General). Civil engineering (General) Borkataky Arunabh verfasserin aut Reddy Sneha verfasserin aut Thirunavukkarasu I. verfasserin aut In MATEC Web of Conferences EDP Sciences, 2013 153, p 07004(2018) (DE-627)720166209 (DE-600)2673602-0 2261236X nnns volume:153, p 07004 year:2018 https://doi.org/10.1051/matecconf/201815307004 kostenfrei https://doaj.org/article/3694815e8fd744d1a421f8774d18ca5e kostenfrei https://doi.org/10.1051/matecconf/201815307004 kostenfrei https://doaj.org/toc/2261-236X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 153, p 07004 2018
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source	In MATEC Web of Conferences 153, p 07004(2018) volume:153, p 07004 year:2018
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callnumber-first	T - Technology
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title	Measurement of Global Solar Radiation data using Raspberry Pi and its estimation using Genetic Algorithm
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title_full	Measurement of Global Solar Radiation data using Raspberry Pi and its estimation using Genetic Algorithm
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title_auth	Measurement of Global Solar Radiation data using Raspberry Pi and its estimation using Genetic Algorithm
abstract	The demand for more efficient and environmentally benign, non-conventional sources of energy came into picture due to increasing demands for human comforts. Solar energy is now the ultimate option. In this paper, the instruments used to measure the solar radiation at Innovation Centre, MIT Manipal were connected to a Raspberry Pi to access the data remotely. Genetic Algorithms were formulated, so that the monthly mean global solar radiation in Manipal can be effectively estimated. Meteorological data such as humidity, temperature, wind speed, etc. were used as inputs to train the networks. A successful network was made between the data loggers and the Raspberry Pi. The data collected by the data loggers from the devices are transmitted to the Raspberry Pi which in turn sends the data to an internal server. The Raspberry Pi can be accessed using any SSH client such as PuTTY. The meteorological data was collected for the years 2010-2014 in order to formulate the Artificial Intelligence models. The validity of the formulated models were checked by comparing the measured data with the estimated data using tools such as RMSE, correlation coefficient, etc. The modelling of solar radiation using GA was carried out in GeneXpro tools version 5.0.
abstractGer	The demand for more efficient and environmentally benign, non-conventional sources of energy came into picture due to increasing demands for human comforts. Solar energy is now the ultimate option. In this paper, the instruments used to measure the solar radiation at Innovation Centre, MIT Manipal were connected to a Raspberry Pi to access the data remotely. Genetic Algorithms were formulated, so that the monthly mean global solar radiation in Manipal can be effectively estimated. Meteorological data such as humidity, temperature, wind speed, etc. were used as inputs to train the networks. A successful network was made between the data loggers and the Raspberry Pi. The data collected by the data loggers from the devices are transmitted to the Raspberry Pi which in turn sends the data to an internal server. The Raspberry Pi can be accessed using any SSH client such as PuTTY. The meteorological data was collected for the years 2010-2014 in order to formulate the Artificial Intelligence models. The validity of the formulated models were checked by comparing the measured data with the estimated data using tools such as RMSE, correlation coefficient, etc. The modelling of solar radiation using GA was carried out in GeneXpro tools version 5.0.
abstract_unstemmed	The demand for more efficient and environmentally benign, non-conventional sources of energy came into picture due to increasing demands for human comforts. Solar energy is now the ultimate option. In this paper, the instruments used to measure the solar radiation at Innovation Centre, MIT Manipal were connected to a Raspberry Pi to access the data remotely. Genetic Algorithms were formulated, so that the monthly mean global solar radiation in Manipal can be effectively estimated. Meteorological data such as humidity, temperature, wind speed, etc. were used as inputs to train the networks. A successful network was made between the data loggers and the Raspberry Pi. The data collected by the data loggers from the devices are transmitted to the Raspberry Pi which in turn sends the data to an internal server. The Raspberry Pi can be accessed using any SSH client such as PuTTY. The meteorological data was collected for the years 2010-2014 in order to formulate the Artificial Intelligence models. The validity of the formulated models were checked by comparing the measured data with the estimated data using tools such as RMSE, correlation coefficient, etc. The modelling of solar radiation using GA was carried out in GeneXpro tools version 5.0.
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title_short	Measurement of Global Solar Radiation data using Raspberry Pi and its estimation using Genetic Algorithm
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Measurement of Global Solar Radiation data using Raspberry Pi and its estimation using Genetic Algorithm

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