Zhou, Hong and Su, Gang and Li, Guofang (2011) Forecasting daily gas load with OIHF-Elman neural network. In: 2nd International Conference on Ambient Systems, Networks and Technologies (ANT 2011) , 19-21 Sep 2011, Niagara Falls, ON. Canada.
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.
|Item Type:||Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)|
|Additional Information:||Permanent restricted access to published version due to publisher copyright policy.|
|Uncontrolled Keywords:||OIHF-Elman neural network; daily gas load; forecasting|
|Depositing User:||Dr Hong Zhou|
|Date Deposited:||17 Jun 2012 23:59|
|Last Modified:||03 Jul 2013 00:59|
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