ID:
publications-4372
Type:
article
Year:
1993
Authors:
Shvartser, Leonid and Shvartser, Leonid and Shamir, Uri and Shamir, Uri and Feldman, Mordechai and Feldman, Mordechai
Title:
Forecasting Hourly Water Demands by Pattern Recognition Approach
Venue/Journal:
Journal of Water Resources Planning and Management
DOI:
10.1061/(asce)0733-9496(1993)119:6(611)
Research type:
Water System:
Technical Focus:
Abstract:
Hourly waterβ€demand data is forecasted with a model based on a combination of pattern recognition and timeβ€series analysis. Three repeating segments are observed in the daily demand pattern: β€_x009c_rising,β€_x009d_ β€_x009c_oscillating,β€_x009d_ β€_x009c_falling,β€_x009d_ then β€_x009c_risingβ€_x009d_ again the following day. These are called β€_x009c_statesβ€_x009d_ of the demand curve, and are defined as successive states of a Markov process. The transition probabilities between states are β€_x009c_learned,β€_x009d_ and lowβ€order autoβ€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.
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