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Application of artificial neural network to model the energy output of dairy farms in Iran

Author

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  • Paria Sefeedpari
  • Shahin Rafiee
  • Asadollah Akram

Abstract

An artificial neural network (ANN) model was developed to assess the energy input-output prediction in dairy farms of Iran. Data used were culled from 50 randomly selected farms using face to face questionnaire approach. The energy input-output analysis was carried out for the parameters of ANN model. Based on performance measures, single hidden layers with 16 neurons in the hidden layer were finally selected as the best configuration for predicting energy output. In this study, we calculated total energy input and output to be 53,102 and 58,315 MJ cow−1, respectively. The predicted values of the best and optimal structure of ANN model were correlated well with actual values with coefficient of determination (R2) of 0.88 and root mean square error (RMSE) of 0.015. Therefore, since the ANN model can accurately predict the derived energy output of milk production system, it could be alternated by other predicting approaches such as regression.

Suggested Citation

  • Paria Sefeedpari & Shahin Rafiee & Asadollah Akram, 2013. "Application of artificial neural network to model the energy output of dairy farms in Iran," International Journal of Energy Technology and Policy, Inderscience Enterprises Ltd, vol. 9(1), pages 82-91.
    • Handle: RePEc:ids:ijetpo:v:9:y:2013:i:1:p:82-91
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    Cited by:

    1. Shine, P. & Scully, T. & Upton, J. & Murphy, M.D., 2019. "Annual electricity consumption prediction and future expansion analysis on dairy farms using a support vector machine," Applied Energy, Elsevier, vol. 250(C), pages 1110-1119.
    2. Philip Shine & John Upton & Paria Sefeedpari & Michael D. Murphy, 2020. "Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses," Energies, MDPI, vol. 13(5), pages 1-25, March.

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