Forecasting Hourly Water Demands by Pattern Recognition Approach

Abstract

Hourly water‐demand data is forecasted with a model based on a combination of pattern recognition and time‐series analysis. Three repeating segments are observed in the daily demand pattern: “rising,” “oscillating,” “falling,” then “rising” again the following day. These are called “states” of the demand curve, and are defined as successive states of a Markov process. The transition probabilities between states are “learned,” and low‐order auto‐regressive integrated moving average (ARIMA) models fitted to each segment, using a modest amount of historical data. The model is then used to forecast hourly demands for a period of one to several days ahead. The forecast can be performed in real time, on a personal computer, with low computational requirements, at any time the system state deviates from the planned, or when new data become available. The process of model development, application, and evaluation is demonstrated on a water system in Israel.