Water Consumption Prediction by Using SVM and Information Granular Time Series Regression
The accuracy of water consumption prediction has a direct effect on the practicality of optimal operation and control in a water distribution network system.In this paper,Support Vector Machine (SVM) was applied to conduct simulation training for hour-ly water consumption data (over a period of 11 months) which had been processed by fuzzy information granulation method,and water consumption for the following month was predicted.First,the maximum hourly water consumption was extracted from the previous 11 months’ water consumption.Then every seven data(a week) were transformed to a triangle fuzzy particle.Three parameters of the triangle fuzzy particle,Low,R,and Up were defined as the minimum change (Low),the average change (R) and the maximum change (Up) of the maximum water consumption dur-ing one week.Finally,SVM was used to predict these 3 parameters and the maxi-mum hourly water consumption.In order to solve the SVM,problems occurred while adjusting related parameters,and optimization selection via genetic algorithm was proposed.The results in actual application indicated that this model had faster speed,higher precision and practicality.
Information Granular Support Vector Machine Grid Algorithm Water Consumption Prediction
Ming Zhao Qingzhou Zhang Yuan Huang Jian Gao Hongbin Zhao
Professor, Municipal Faculty, Harbin Institute of Technology; No.73, Huanghe Road, Harbin 150090 Master, School of Municipal and Environmental Engineering, Second Campus, Harbin Institute of Techno Engineer, Tianjin Sambo Water Technology Co., Ltd; Room1105, Wanrong Build-ing, Yichang Road, Tianji
国际会议
北京
英文
482-491
2012-10-14（万方平台首次上网日期，不代表论文的发表时间）