Application of weighted composition model in urban water consumption forecasting

被引：0
|
作者：
Wang P. [1 ]
Chen R. [1 ]
Sun X. [2 ]
Wei X. [3 ]
机构：
[1] Key Laboratory of the Three Gorges Reservoir Region's Eco-Environment, Ministry of Education, Chongqing University
[2] Faculty of Mathematics and Physics of Chongqing University
[3] Suining Mingxing Water Supply Company
来源：
Yingyong Jichu yu Gongcheng Kexue Xuebao/Journal of Basic Science and Engineering | 2010年 / 18卷 / 03期
关键词：
Nonlinear model; Quadratic programming; Unbiased grey model; Weighted composition model;
D O I：
10.3969/j.issn.1005-0930.2010.03.007
中图分类号：
学科分类号：
摘要：
In view of the inherent deviation existing in the traditional grey model during modeling, this article used unbiased grey GM(1, 1) forecasting model. Based on the expression of the unbiased grey forecasting model, the nonlinear forecasting model was put forward and applied for forecasting urban water consumption. Considering the deficiency of the single forecasting model in the course of forecast, the unbiased grey GM(1, 1) forecasting model was combined with nonlinear model by weighted composition mode, which was firstly applied for forecasting urban water consumption of Suining city, Sichuan province. The prediction results indicated that the combination model was superior than the single model in predictive precision and the results show that this method provides a better fit to the real urban water consumption, which can be popularized to the water consumption prediction of other similar cities.
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页码：428 / 434
页数：6
相关论文
共 13 条
  • [1] Li L., Zuo Q., Comparative research on predicting method and application for city water consumption, Journal of Water Resources and Water Engineering, 16, 3, pp. 6-10, (2005)
  • [2] Yuan Y., Zhang J., Xu H., Et al., Study on medium and long-term forecasting model of urban water consumption, Water and Wastewater Engineering, 30, 6, pp. 102-105, (2004)
  • [3] Zhang X., Dang Z., Zhang X., Et al., Comprehensive research on forecasting model of municipal water consumption, Journal of Water Resources and Water Engineering, 16, 4, pp. 24-28, (2005)
  • [4] Zhang Z., Chen C., Zhang Y., The headway made in application of artificial neural network in the forecasting of urban water consumption, Water and Wastewater Engineering, S1, 32, pp. 110-114, (2007)
  • [5] Aly A.H., Wanakule N., Short-term forecasting for urbanwater consumption, Journal of Water Resources Planning and Management, 130, 5, pp. 405-410, (2004)
  • [6] Batea J.M., Granger C.W.J., Combination of forcast, Operations Research Quarterly, 20, 4, pp. 451-468, (1969)
  • [7] Paulo S.A., Freitas Antonio J., Rodrigues L., Model combination in neural-based forecasting, European Journal of Operational Research, 173, pp. 801-814, (2006)
  • [8] Xiong G., Chen Z., The shortcoming of grey forcasting model and a modified method for it, Systems Engineering, 10, 6, pp. 32-36, (1992)
  • [9] Li G., Zhu J., Xu K., Et al., The application and study in electric load forecasting with no deviation grey model, Journal of Harbin University of Science and Technology, 10, 2, pp. 87-88, (2005)
  • [10] Yin X., Han D., The Design of Experiment and Data Processing, pp. 46-49, (2006)
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