Forecasting Daily Gas Load with OIHF-Elman Neural Network | Zendy

Zhou Hong | Zendy; Gang Su | Zendy; Guofang Li | Zendy

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Forecasting Daily Gas Load with OIHF-Elman Neural Network

Author(s) -

Zhou Hong,

Gang Su,

Guofang Li

Publication year - 2011

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2011.07.100

Subject(s) - computer science , generalization , artificial neural network , sampling (signal processing) , artificial intelligence , machine learning , data mining , telecommunications , mathematical analysis , mathematics , detector

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

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