عنوان مقاله [English]
In recent years many researches have been performed on the subject of optimal usage of water resources. One of the issues in this regard is preparing short term water demand forecasting in conjunction with the optimum water demand management. Considering the effects of climatologic conditions on the short term water demand and according to the similarity of consumption trends in the consecutive days, two conventional and advanced models have been developed in this paper using time series method. These models have been used to predict the short term water demand in Tehran, Iran. In the conventional model the time series of Tehran daily water consumption is divided into different components of trend, seasonal variations, and random variations which are obtained by regression method. In the advanced model the consumption pattern in the past is recognized using combined methods of Auto Regressive (AR) and Seasonal Moving Average. Assuming that this pattern will continue in the future, it is then applied to predict daily water demand. In the conventional models it is assumed that different components of time series are independent and dividable. Therefore, its components are determined by different methods such as regression, moving average, etc. In the advanced models all the components are supposed to be correlated to each other and are therefore analyzed together. Comparing the results with the real data showed the ability and accuracy of the both time series models to predict the Tehran daily water demand. The advanced models produced better results than the conventional methods.