Forecasting daily gas load with OIHF-Elman neural network

Zhou, Hong and Su, Gang and Li, Guofang (2011) Forecasting daily gas load with OIHF-Elman neural network. Procedia Computer Science, 5. pp. 754-758.


To improve the forecasting accuracy, a model for forecasting daily gas load with OIHF-Elman network involving factors such as weather, temperature and data type is proposed. Compared with the conventional Elman network, OIHF-Elman network considers not only the hidden level feedback but also the output level feedbacks. Therefore more information from limited sampling spots is collected and utilized. The simulation results show that OIHF-Elman network performs better than Elman network in terms of accuracy given limited sampling points. The new model also improves the generalization of information and can be used to forecast the daily gas load successfully.

Statistics for USQ ePrint 20464
Statistics for this ePrint Item
Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2011 Published by Elsevier Ltd. Permanent restricted access to published version due to publisher copyright policy. First presented at: 2nd International Conference on Ambient Systems, Networks and Technologies (ANT 2011)
Faculty / Department / School: Historic - Faculty of Engineering and Surveying - Department of Electrical, Electronic and Computer Engineering
Date Deposited: 17 Jun 2012 23:59
Last Modified: 20 Feb 2015 05:05
Uncontrolled Keywords: OIHF-Elman neural network; daily gas load; forecasting
Fields of Research : 01 Mathematical Sciences > 0102 Applied Mathematics > 010206 Operations Research
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
14 Economics > 1403 Econometrics > 140305 Time-Series Analysis
Socio-Economic Objective: D Environment > 96 Environment > 9606 Environmental and Natural Resource Evaluation > 960604 Environmental Management Systems
Identification Number or DOI: 10.1016/j.procs.2011.07.100

Actions (login required)

View Item Archive Repository Staff Only