Linear Regression Model for Estimating Sustainable Generation: A Case Study in Tamil Nadu.
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| Title: | Linear Regression Model for Estimating Sustainable Generation: A Case Study in Tamil Nadu. |
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| Authors: | Geetha, A.1, Usha, S.1, Santhakumar, J.2 Santhakj@srmist.edu.in, Kalash, Amrit3, Saini, Harshit3, Sinha, Shashwat3 |
| Source: | EAI Endorsed Transactions on the Energy Web. 2022, Vol. 9 Issue 37, p1-6. 6p. |
| Subject Terms: | *Regression analysis data processing, *Renewable energy industry, *DC-AC converters, *Photovoltaic power generation, *Solar radiation |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 154006903 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Linear Regression Model for Estimating Sustainable Generation: A Case Study in Tamil Nadu. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Geetha%2C+A%2E%22">Geetha, A.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Usha%2C+S%2E%22">Usha, S.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Santhakumar%2C+J%2E%22">Santhakumar, J.</searchLink><relatesTo>2</relatesTo><i> Santhakj@srmist.edu.in</i><br /><searchLink fieldCode="AR" term="%22Kalash%2C+Amrit%22">Kalash, Amrit</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Saini%2C+Harshit%22">Saini, Harshit</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Sinha%2C+Shashwat%22">Sinha, Shashwat</searchLink><relatesTo>3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22EAI+Endorsed+Transactions+on+the+Energy+Web%22">EAI Endorsed Transactions on the Energy Web</searchLink>. 2022, Vol. 9 Issue 37, p1-6. 6p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Regression+analysis+data+processing%22">Regression analysis data processing</searchLink><br />*<searchLink fieldCode="DE" term="%22Renewable+energy+industry%22">Renewable energy industry</searchLink><br />*<searchLink fieldCode="DE" term="%22DC-AC+converters%22">DC-AC converters</searchLink><br />*<searchLink fieldCode="DE" term="%22Photovoltaic+power+generation%22">Photovoltaic power generation</searchLink><br />*<searchLink fieldCode="DE" term="%22Solar+radiation%22">Solar radiation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=154006903 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.4108/eai.8-7-2021.170289 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 6 StartPage: 1 Subjects: – SubjectFull: Regression analysis data processing Type: general – SubjectFull: Renewable energy industry Type: general – SubjectFull: DC-AC converters Type: general – SubjectFull: Photovoltaic power generation Type: general – SubjectFull: Solar radiation Type: general Titles: – TitleFull: Linear Regression Model for Estimating Sustainable Generation: A Case Study in Tamil Nadu. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Geetha, A. – PersonEntity: Name: NameFull: Usha, S. – PersonEntity: Name: NameFull: Santhakumar, J. – PersonEntity: Name: NameFull: Kalash, Amrit – PersonEntity: Name: NameFull: Saini, Harshit – PersonEntity: Name: NameFull: Sinha, Shashwat IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 2032944X Numbering: – Type: volume Value: 9 – Type: issue Value: 37 Titles: – TitleFull: EAI Endorsed Transactions on the Energy Web Type: main |
| ResultId | 1 |
