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Modelling of Energy Consumption in Wheat Production Using Neural Networks “Case Study in Canterbury Province, New Zealand“

Authors: M. Safa, S. Samarasinghe

Abstract:

An artificial neural network (ANN) approach was used to model the energy consumption of wheat production. This study was conducted over 35,300 hectares of irrigated and dry land wheat fields in Canterbury in the 2007-2008 harvest year.1 In this study several direct and indirect factors have been used to create an artificial neural networks model to predict energy use in wheat production. The final model can predict energy consumption by using farm condition (size of wheat area and number paddocks), farmers- social properties (education), and energy inputs (N and P use, fungicide consumption, seed consumption, and irrigation frequency), it can also predict energy use in Canterbury wheat farms with error margin of ±7% (± 1600 MJ/ha).

Keywords: Artificial neural network, Canterbury, energy consumption, modelling, New Zealand, wheat.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1084418

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References:


[1] Pellizzi G. Use of energy and labour in Italian agriculture. Journal of Agricultural Engineering Research. 1992;52:111-9.
[2] Al-Ghandoor A, Jaber JO, Al-Hinti I, Mansour IM. Residential past and future energy consumption: Potential savings and environmental impact. Renewable and Sustainable Energy Reviews. 2009;13(6-7):1262-74.
[3] Tester JW. Sustainable energy : choosing among options. Cambridge, Mass.: MIT Press, 2005.
[4] Sözen A. Future projection of the energy dependency of Turkey using artificial neural network. Energy Policy. 2009;37(11):4827-33.
[5] Jebaraj S, Iniyan S. A review of energy models. Renewable and Sustainable Energy Reviews. 2006;10(4):281-311.
[6] Fang Q, Hanna MA, Haque E, Spillman CK. Neural Network Modeling Of Energy Requirments, For Size Reduction Of Wheat. ASAE. 2000;43(4): 947-952.
[7] Hornik K, Stinchocombe M, White H. Multilayer feedforward networks are universal approximators. Elsevier Science Ltd Oxford, UK, UK 1989;2(5).
[8] Heinzow T, Tol RSJ. Prediction of crop yields across four climate zones in Germany: an artificial neural network approach. Centre for Marine and Climate Research, Hamburg University, Hamburg. 2003.
[9] Kalogirou SA. Artificial neural networks in renewable energy systems applications: a review. Renewable and Sustainable Energy Reviews. 2001;5(4):373-401.
[10] Javeed Nizami S, Al-Garni AZ. Forecasting electric energy consumption using neural networks. Energy Policy. 1995;23(12):1097-104.
[11] Mohandes MA, Rehman S, Halawani TO. A neural networks approach for wind speed prediction. Renewable Energy. 1998;13(3):345-54.
[12] Kalogirou SA, Bojic M. Artificial neural networks for the prediction of the energy consumption of a passive solar building. energy. 2000;25(5):479-91.
[13] Kalogirou SA. Applications of artificial neural-networks for energy systems. Applied Energy. 2000;67(1-2):17-35.
[14] Aydinalp M, Ismet Ugursal V, Fung AS. Modeling of the appliance, lighting, and space-cooling energy consumptions in the residential sector using neural networks. Applied Energy. 2002;71(2):87-110.
[15] Hsu C-C, Chen C-Y. Regional load forecasting in Taiwan--applications of artificial neural networks. Energy Conversion and Management. 2003;44(12):1941-9.
[16] Hagan M, Demuth H, Beale M. Neural network design: Boston, USA: PWS Publishing Company, 2002.
[17] Samarasinghe S. Neural networks for applied sciences and engineering : from fundamentals to complex pattern recognition. Boca Raton, FL: Auerbach, 2007.