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Assessment of Solar Energy Potential of Smart Cities of Tamil Nadu Using Machine Learning with Big Data

  • R. MeenalEmail author
  • A. Immanuel Selvakumar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 750)

Abstract

Global Solar Radiation (GSR) prediction is important to forecast the output power of solar PV system in case of renewable energy integration into the existing grid. GSR can be predicted using commonly measured meteorological data like relative humidity, maximum, and minimum temperature as input. The input data is collected from India Meteorological Department (IMD), Pune. In this work, Waikato Environment for Knowledge Analysis (WEKA) software is employed for GSR prediction using Machine Learning (ML) techniques integrated with Big Data. Feature selection methodology is used to reduce the input data set which improves the prediction accuracy and helps the algorithm to run fast. Predicted GSR value is compared with measured value. Out of eight ML algorithms, Random Forest (RF) has minimum errors. Hence this work attempts in predicting the GSR in Tamil Nadu using RF algorithm. The predicted GSR values are in the range of 5–6 kWh/m2/day for various solar energy applications in Tamil Nadu.

Keywords

Machine learning Big data Global solar radiation Random forest 

Notes

Acknowledgements

Authors would like to thank IMD, Pune for data support.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Electrical SciencesKarunya Institute of Technology and SciencesCoimbatoreIndia

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