Application of artificial neural network to model the energy output of dairy farms in Iran Online publication date: Sat, 21-Jun-2014
by Paria Sefeedpari; Shahin Rafiee; Asadollah Akram
International Journal of Energy Technology and Policy (IJETP), Vol. 9, No. 1, 2013
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Energy Technology and Policy (IJETP):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook