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Developing an integrated approach for optimum prediction and forecasting of renewable and non-renewable energy consumption in Iran

Author

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  • Reza Babazadeh
  • Shima Pashapour
  • Abbas Keramati

Abstract

Energy planning for mid and long term periods needs forecasting the energy demands in the future. The artificial neural network (ANN) is an efficient forecasting tool which have been widely applied in different fields. One of the weaknesses of the ANN method is appeared when the studied case has many input parameters affecting on the performance of output factor. Noteworthy, there is not reliable data in many applications of real world. The canonical correlation analysis (CCA) method is an efficient tool for data reduction purpose keeping useful information of the used data. The purpose of this paper is to estimate and predict the renewable and non-renewable energy consumption considering environmental and economic factors. To this aim, an integrated approach based on the CCA and ANN method is utilised. The results show that the proposed approach reduces dimension of data without losing valuable information.

Suggested Citation

  • Reza Babazadeh & Shima Pashapour & Abbas Keramati, 2020. "Developing an integrated approach for optimum prediction and forecasting of renewable and non-renewable energy consumption in Iran," International Journal of Energy Technology and Policy, Inderscience Enterprises Ltd, vol. 16(2), pages 119-135.
    • Handle: RePEc:ids:ijetpo:v:16:y:2020:i:2:p:119-135
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    Cited by:

    1. Hadi Fazeli & Mohammad Sadegh Allahyari & Saeid Firouzi & Tarek Ben Hassen & Jhalukpreya Surujlal & Nima Nejadrezaei & Mina Sadeghzadeh, 2023. "Knowledge, Attitude, and Perception of Students Regarding Renewable Energies in Agriculture in Guilan, Iran," Agriculture, MDPI, vol. 13(8), pages 1-16, August.

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