Forecasting Hourly Water Demands by Pattern Recognition Approach
| Autor: | Leonid Shvartser, Mordechai Feldman, Uri Shamir |
|---|---|
| Rok vydání: | 1993 |
| Předmět: |
Engineering
Markov chain business.industry Geography Planning and Development Markov process Pattern recognition Management Monitoring Policy and Law symbols.namesake Moving average Demand curve Personal computer Pattern recognition (psychology) Econometrics symbols Artificial intelligence Autoregressive integrated moving average Time series business Water Science and Technology Civil and Structural Engineering |
| Zdroj: | Journal of Water Resources Planning and Management. 119:611-627 |
| ISSN: | 1943-5452 0733-9496 |
| DOI: | 10.1061/(asce)0733-9496(1993)119:6(611) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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