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Solar energy potential assessment of western Himalayan Indian state of Himachal Pradesh using J48 algorithm of WEKA in ANN based prediction model

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  • Yadav, Amit Kumar
  • Chandel, S.S.

Abstract

Solar potential of western Himalayan Indian state of Himachal Pradesh is assessed using Artificial Neural Network (ANN) based global solar radiation (GSR) prediction model. J48 algorithm in Waikato Environment for Knowledge Analysis (WEKA)is used for the selection of input parameters for ANN model for predicting GSR. Most relevant input parameters are found to be temperature, altitude and sunshine hours whereas latitude, longitude, clearness index and extraterrestrial radiation are found to be least influencing variables. The usefulness of J48 algorithm in variable selection is checked by developing five ANN models: ANN-1, ANN-2, ANN-3, ANN-4 and ANN-5. The maximum mean absolute percentage error (MAPE) for ANN-1, ANN-2, ANN-3, ANN-4 and ANN-5 are found to be 16.91%, 16.89%, 16.38%, 6.89% and 9.04% respectively. ANN-5 model is used to develop the solar maps of Himachal Pradesh. The estimated GSR varies from 3.59 to 5.38 kWh/m2/day indicating good solar potential for solar energy applications. A correlation is developed between NASA satellite data and ground measured GSR data to find values close to ground measured GSR for different locations. The correlation coefficient is found to be 0.97. Models developed can be used to assess solar potential of any location worldwide.

Suggested Citation

  • Yadav, Amit Kumar & Chandel, S.S., 2015. "Solar energy potential assessment of western Himalayan Indian state of Himachal Pradesh using J48 algorithm of WEKA in ANN based prediction model," Renewable Energy, Elsevier, vol. 75(C), pages 675-693.
  • Handle: RePEc:eee:renene:v:75:y:2015:i:c:p:675-693
    DOI: 10.1016/j.renene.2014.10.046
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    References listed on IDEAS

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    1. López, G. & Batlles, F.J. & Tovar-Pescador, J., 2005. "Selection of input parameters to model direct solar irradiance by using artificial neural networks," Energy, Elsevier, vol. 30(9), pages 1675-1684.
    2. Rumbayan, Meita & Abudureyimu, Asifujiang & Nagasaka, Ken, 2012. "Mapping of solar energy potential in Indonesia using artificial neural network and geographical information system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(3), pages 1437-1449.
    3. Alam, Shah & Kaushik, S.C. & Garg, S.N., 2009. "Assessment of diffuse solar energy under general sky condition using artificial neural network," Applied Energy, Elsevier, vol. 86(4), pages 554-564, April.
    4. Ouammi, Ahmed & Zejli, Driss & Dagdougui, Hanane & Benchrifa, Rachid, 2012. "Artificial neural network analysis of Moroccan solar potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4876-4889.
    5. Rehman, Shafiqur & Mohandes, Mohamed, 2008. "Artificial neural network estimation of global solar radiation using air temperature and relative humidity," Energy Policy, Elsevier, vol. 36(2), pages 571-576, February.
    6. Mahtta, Richa & Joshi, P.K. & Jindal, Alok Kumar, 2014. "Solar power potential mapping in India using remote sensing inputs and environmental parameters," Renewable Energy, Elsevier, vol. 71(C), pages 255-262.
    7. Yadav, Amit Kumar & Malik, Hasmat & Chandel, S.S., 2014. "Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 509-519.
    8. Bosch, J.L. & López, G. & Batlles, F.J., 2008. "Daily solar irradiation estimation over a mountainous area using artificial neural networks," Renewable Energy, Elsevier, vol. 33(7), pages 1622-1628.
    9. Diagne, Maimouna & David, Mathieu & Lauret, Philippe & Boland, John & Schmutz, Nicolas, 2013. "Review of solar irradiance forecasting methods and a proposition for small-scale insular grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 65-76.
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    More about this item

    Keywords

    Solar potential; Global solar radiation; Artificial neural network; J48 algorithm; Western Himalayas;
    All these keywords.

    JEL classification:

    • J48 - Labor and Demographic Economics - - Particular Labor Markets - - - Particular Labor Markets; Public Policy

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