Abstract: In view of the cyclical and volatile characteristics of urban water use,based on the monthly water supply sequence of 47 waterworks and 10 administrative regions in Shenzhen from 2015 to 2019. Considering the industrial structure,demographic characteristics,and time change factors of water supply,a coupling method of the KMeans clustering algorithm and seasonal moving average auto regressive（SMAAR）model is proposed. The model can cluster the time series of waterworks and administrative regions and model by category to forecast monthly water supply data for waterworks and administrative regions from January to August 2020. Then the total water supply data of Shenzhen from January to August 2020 is summarized. The results show that the smaller the scope of the modeling object,the smaller the relative error（RE）of the predicted result. The performance of SMAAR is significantly improved compared to ARMA,and it still shows strong generalization ability in the long-term forecast. The average RE of the daily forecast results for 254 days is only 0.08. The method of this study can provide a reference for water demand forecasting and water supply scheduling management in other cities.