Linear Regression Model for Estimating Sustainable Generation: A Case Study in Tamil Nadu

This article aims at developing a statistical model for the prediction of DC and AC generated power from the installed PV plant. A proper understanding of the PV plant characteristics is highly in need of predicting the yield based on the solar and atmospheric parameters. Thi...

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Autores principales:	A. Geetha, S. Usha, J. Santhakumar, Amrit Kalash, Harshit Saini, Shashwat Sinha
Formato:	article
Lenguaje:	EN
Publicado:	European Alliance for Innovation (EAI) 2022
Materias:	pv plant meteorological information prediction solar irradiance regression model Science Q Mathematics QA1-939 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/674092f9a3ac4e9882a4b5941ad4c1dc
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spelling	oai:doaj.org-article:674092f9a3ac4e9882a4b5941ad4c1dc2021-11-30T11:07:32ZLinear Regression Model for Estimating Sustainable Generation: A Case Study in Tamil Nadu2032-944X10.4108/eai.8-7-2021.170289https://doaj.org/article/674092f9a3ac4e9882a4b5941ad4c1dc2022-01-01T00:00:00Zhttps://eudl.eu/pdf/10.4108/eai.8-7-2021.170289https://doaj.org/toc/2032-944XThis article aims at developing a statistical model for the prediction of DC and AC generated power from the installed PV plant. A proper understanding of the PV plant characteristics is highly in need of predicting the yield based on the solar and atmospheric parameters. This study focusses on investigating the relationship among the factors such as beam and diffused solar radiations, atmospheric temperature and wind speed for predicting the hourly generated powers. The location involved in the investigation is Chennai city, Tamil Nadu state, India. The meteorological data for the selected location is obtained from NREL and using a simple linear regression model prediction equations for DC and AC solar output power was built using Minitab 16.2.1 version. The methodology used has a capability of better correlation coefficient than the other techniques. The developed regression models show R2 value of 99.24% and 99% for DC and AC power and the predicted R2 (Rpred) values obtained are 86.54% and 83.22% for DC and AC power respectively.A. GeethaS. UshaJ. SanthakumarAmrit KalashHarshit SainiShashwat SinhaEuropean Alliance for Innovation (EAI)articlepv plantmeteorological informationpredictionsolar irradianceregression modelScienceQMathematicsQA1-939Electronic computers. Computer scienceQA75.5-76.95ENEAI Endorsed Transactions on Energy Web, Vol 9, Iss 37 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	pv plant meteorological information prediction solar irradiance regression model Science Q Mathematics QA1-939 Electronic computers. Computer science QA75.5-76.95
spellingShingle	pv plant meteorological information prediction solar irradiance regression model Science Q Mathematics QA1-939 Electronic computers. Computer science QA75.5-76.95 A. Geetha S. Usha J. Santhakumar Amrit Kalash Harshit Saini Shashwat Sinha Linear Regression Model for Estimating Sustainable Generation: A Case Study in Tamil Nadu
description	This article aims at developing a statistical model for the prediction of DC and AC generated power from the installed PV plant. A proper understanding of the PV plant characteristics is highly in need of predicting the yield based on the solar and atmospheric parameters. This study focusses on investigating the relationship among the factors such as beam and diffused solar radiations, atmospheric temperature and wind speed for predicting the hourly generated powers. The location involved in the investigation is Chennai city, Tamil Nadu state, India. The meteorological data for the selected location is obtained from NREL and using a simple linear regression model prediction equations for DC and AC solar output power was built using Minitab 16.2.1 version. The methodology used has a capability of better correlation coefficient than the other techniques. The developed regression models show R2 value of 99.24% and 99% for DC and AC power and the predicted R2 (Rpred) values obtained are 86.54% and 83.22% for DC and AC power respectively.
format	article
author	A. Geetha S. Usha J. Santhakumar Amrit Kalash Harshit Saini Shashwat Sinha
author_facet	A. Geetha S. Usha J. Santhakumar Amrit Kalash Harshit Saini Shashwat Sinha
author_sort	A. Geetha
title	Linear Regression Model for Estimating Sustainable Generation: A Case Study in Tamil Nadu
title_short	Linear Regression Model for Estimating Sustainable Generation: A Case Study in Tamil Nadu
title_full	Linear Regression Model for Estimating Sustainable Generation: A Case Study in Tamil Nadu
title_fullStr	Linear Regression Model for Estimating Sustainable Generation: A Case Study in Tamil Nadu
title_full_unstemmed	Linear Regression Model for Estimating Sustainable Generation: A Case Study in Tamil Nadu
title_sort	linear regression model for estimating sustainable generation: a case study in tamil nadu
publisher	European Alliance for Innovation (EAI)
publishDate	2022
url	https://doaj.org/article/674092f9a3ac4e9882a4b5941ad4c1dc
work_keys_str_mv	AT ageetha linearregressionmodelforestimatingsustainablegenerationacasestudyintamilnadu AT susha linearregressionmodelforestimatingsustainablegenerationacasestudyintamilnadu AT jsanthakumar linearregressionmodelforestimatingsustainablegenerationacasestudyintamilnadu AT amritkalash linearregressionmodelforestimatingsustainablegenerationacasestudyintamilnadu AT harshitsaini linearregressionmodelforestimatingsustainablegenerationacasestudyintamilnadu AT shashwatsinha linearregressionmodelforestimatingsustainablegenerationacasestudyintamilnadu
_version_	1718406686768627712

Linear Regression Model for Estimating Sustainable Generation: A Case Study in Tamil Nadu

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