Short-term water demand forecasting algorithm based on kalman filtering with data mining

被引：0

作者：

Choi, Gee-Seon

Shin, Gang-Wook

Lim, Sang-Heui

Chun, Myung-Geun

机构：

来源：

Journal of Institute of Control, Robotics and Systems | 2009年 / 15卷 / 10期

关键词：

Data mining; Demand forecasting; Kalman filtering; Water supply system;

D O I：

10.5302/J.ICROS.2009.15.10.1056

中图分类号：

学科分类号：

摘要：

This paper proposes a short-term water demand forecasting algorithm based on kalman filtering with data mining for sustainable water supply and effective energy saving. The proposed algorithm utilizes a mining method of water supply data and a decision tree method with special days like Chuseok. And the parameters of MLAR (Multi Linear Auto Regression) model are estimated by Kalman filtering algorithm. Thus, we can achieve the practicality of the proposed forecasting algorithm through the good results applied to actual operation data.

引用

页码：1056 / 1061

页数：5

共 16 条

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