Volume 5, Issue 20 (9-2015)                   jemr 2015, 5(20): 73-106 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

sadeghi S K, mousavian S M. Statistical Analysis and Construction of Prediction Intervals for A Hybrid Neural Network in: A Case Study of Natural Gas Consumption in the Household Sector. jemr. 2015; 5 (20) :73-106
URL: http://jemr.khu.ac.ir/article-1-1059-en.html
Abstract:   (1429 Views)

As one of the important energy forms, natural gas consumption has an upward trend in recent years. Therefore management and planning for provision of it requires prediction of the future consumption. But many of prediction procedures are inherently stochastic therefore it is important to have better knowledge about the robustness of prediction procedures. This paper compares robustness of two prediction procedures Artificial Neural Networks as a nonlinear and ARIMA as a linear model. using resampling method to predict the monthly consumption of natural gas in the household sector. Data spans from 2001-4 to 2012-3, to train the networks, we used genetic algorithms and Particle Swarming Optimization then results were compared using 10-fold method. According to the results, the particle swarm optimization (PSO) outperforms the genetic algorithm. Then we used data from 2001-4 to 2010-3, with resampling by 2000 to predict the  natural gas consumption for the 2001 -4 to 2012-3 and to form critical values. Results show that prediction by a mixed method using ANN and PSO is more robust than ARIMA method.

Full-Text [PDF 818 kb]   (2045 Downloads)    
Type of Study: Applicable | Subject: انرژی، منابع و محیط زیست
Received: 2014/07/9 | Accepted: 2015/02/23 | Published: 2015/09/19

Add your comments about this article : Your username or Email:
Write the security code in the box

Send email to the article author

© 2015 All Rights Reserved | Journal of Economic Modeling Research

Designed & Developed by : Yektaweb