short-term and medium-term gas demand load forecasting by neural networks

the ability of artificial neural network (ann) for estimating the natural gas demand load for the next day and month of the populated cities has shown to be a real  concern. as the most applicable network, the ann with multi-layer back propagation perceptrons is used to approximate functions. throughout the current work, the daily effective temperature is determined, and then the weather data with the gas consumption data of the last days are used for network training. it is shown that nearly 93% and 98.9% of the result is in a good agreement with the real data for the daily gas load forecasting and those of the monthly respectively. these results clearly show the capability of the presented networks. the method, however, can further be developed for prediction of other required information in various industries.