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主管单位 中华人民共和国
工业和信息化部
主办单位 哈尔滨工业大学 主编 冷劲松 国际刊号ISSN 0367-6234 国内刊号CN 23-1235/T

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引用本文:吴珊,宋凌硕,侯本伟,寇晓霞.基于Bayesian-LSSVM和残差修正的用户短期需水量预测[J].哈尔滨工业大学学报,2019,51(8):88.DOI:10.11918/j.issn.0367-6234.201807113
WU Shan,SONG Lingshuo,HOU Benwei,KOU Xiaoxia.Short-term water demand forecast based on Bayesian least squares support vector machine and residual correction[J].Journal of Harbin Institute of Technology,2019,51(8):88.DOI:10.11918/j.issn.0367-6234.201807113
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基于Bayesian-LSSVM和残差修正的用户短期需水量预测
吴珊,宋凌硕,侯本伟,寇晓霞
(北京工业大学 建筑工程学院,北京 100124)
摘要:
为有效改善供水管网短期需水量预测模型在预测精度和稳定性方面存在的不足,提出在短期需水量预测模型基础上叠加残差预测模型的组合预测建模方法.首先采用贝叶斯最小二乘支持向量机法(Bayesian-LSSVM)建立管网用户需水量时间序列预测模型(BL模型),得到需水量预测初始值;对BL模型得到的需水量预测初始值的残差序列,构建基于贝叶斯最小二乘支持向量机法的混沌时间序列预测模型(RM模型),得到残差预测值;同时将RM模型得到的残差预测值实时补偿到BL模型的需水量预测初始值中,得到经过残差修正的需水量预测值.实例结果表明,RM模型可以准确捕获BL模型需水量预测初始值的残差变化趋势,对其残差序列进行准确预测;在短期需水量预测的精度和稳定性方面,由BL模型和RM模型叠加构成的组合预测模型(BL+RM模型)明显优于单一BL模型;BL+RM模型适用于平均需水量较小、水量波动性较大等不同特点用户的短期需水量预测,可有效满足实际工程的需要.
关键词:  短期需水量预测  残差修正  贝叶斯最小二乘支持向量机  混沌时间序列预测
DOI:10.11918/j.issn.0367-6234.201807113
分类号:TU991.33
文献标识码:A
基金项目:国家水体污染控制与治理科技重大专项(2017ZX07108-002);国家自然科学基金(51508528)
Short-term water demand forecast based on Bayesian least squares support vector machine and residual correction
WU Shan,SONG Lingshuo,HOU Benwei,KOU Xiaoxia
(College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China)
Abstract:
In order to effectively improve the short-term water demand forecasting model of water distribution networks in terms of prediction accuracy and stability, a novel combined prediction modeling method which can predict water demand and residuals simultaneously is proposed in this paper. First, the Bayesian least squares support vector machine method (Bayesian-LSSVM) was used to establish the time series prediction model of user’s water demand (BL model) to obtain the initial water demand prediction values. Then, to predict the residual sequence of the initial water demand prediction values produced by the BL model, a chaotic time series prediction model (RM model) was constructed based on the Bayesian-LSSVM method. At the same time, the predicted residuals produced by the RM model were compensated to the water demand predictions by the BL model to correct the initial water demand prediction values. Results of the case study show that the RM model could accurately capture the change trend of the residual value of the BL model initial prediction values and the residual sequence of the BL model initial water demand prediction values. The combined forecast model consisting of the BL model and the RM model (BL+RM model) was superior to single BL model in the accuracy and stability of short-term water demand forecasting. BL+RM model was applicable for short-term water demand forecasting with different water demand characteristics such as small average water demand and large water volatility, and hence could effectively meet the needs of actual engineering.
Key words:  short-term water demand forecast  residual correction  Bayesian least squares support vector machine  chaotic time series prediction

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