ew 18: e35

Research Article

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

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  • @ARTICLE{10.4108/eai.8-7-2021.170289,
        author={A. Geetha and S. Usha and J. Santhakumar and Amrit Kalash and Harshit Saini and Shashwat Sinha},
        title={Linear Regression Model for Estimating Sustainable Generation: A Case Study in Tamil Nadu},
        journal={EAI Endorsed Transactions on Energy Web: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={EW},
        year={2021},
        month={7},
        keywords={PV plant, Meteorological information, Prediction, Solar irradiance, Regression model},
        doi={10.4108/eai.8-7-2021.170289}
    }
    
  • A. Geetha
    S. Usha
    J. Santhakumar
    Amrit Kalash
    Harshit Saini
    Shashwat Sinha
    Year: 2021
    Linear Regression Model for Estimating Sustainable Generation: A Case Study in Tamil Nadu
    EW
    EAI
    DOI: 10.4108/eai.8-7-2021.170289
A. Geetha1, S. Usha1, J. Santhakumar2,*, Amrit Kalash3, Harshit Saini3, Shashwat Sinha3
  • 1: Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Chennai, India
  • 2: Department of Mechanical Engineering, SRM Institute of Science and Technology, Chennai, India
  • 3: Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Chennai, India
*Contact email: Santhakj@srmist.edu.in

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.