Forecasting daily gas load with OIHF-Elman neural network

Abstract

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. © 2011 Published by Elsevier Ltd.