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Abnormal Data Refinement and Error Percentage Correction Methods for Effective Short-term Hourly Water Demand Forecasting

Joon-Hong Seok, Jeong-Jung Kim, Joon-Yong Lee, Ju-Jang Lee, Young-Joo Song, and Gang-Wook Shin*
International Journal of Control, Automation, and Systems, vol. 12, no. 6, pp.1245-1256, 2014

Abstract : A short-term hourly water demand forecasting algorithm is needed in order to ensure a stable and safe supply of water. Unlike daily or monthly water demand forecasting, there are a large amount of fluctuation of hourly water demand. Hourly water demand is affected by short time period and abnormal data caused by the sensor, communication, and water treatment plant problems. An ef-fective refinement method that detects and corrects the abnormal data among the historical data is needed to achieve accurate and practical hourly water demand forecasting. In this paper, we suggest an abnormal data refinement out of a confidence interval (ADR-CI) method and an error percentage correction (EPC) method. These methods try to distribute and revise the incoming hourly water demand and past water demand data. The proposed methods are verified by the experiments in a real water supply plant during a year.

Keyword : Abnormal data refinement, error percentage correction, water demand forecasting, water supply.

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