基于季节性ARIMA模型的小区供水预测
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摘要
为了减少二次供水设施给小区供水管网所带来的压力,对供水管网运行状况进行更加精确的预测。结合物联网和时间序列分析等技术,通过对供水管网的历史数据的分析,采用季节性ARIMA模型对供水数据进行预测。设计数据预测分析的步骤和方案,建立方差估值为0.404 9、AIC为284.85的ARIMA(3,0,1)×(1,1,1)24模型。实验结果表明,设计的季节性ARIMA模型的预测周期短且有较高的预测精度,能够有效地对供水管网运行状态进行预测。
In order to reduce the pressure on the secondary water supply facilities to the regional water supply network,we need to accurately predict the operation of the water supply network. By using the technology of the Internet of Things and time series,and through the analysis of the historical data of water supply network,a seasonal ARIMA model is used to predict the data. The data analysis steps and programs were designed,and the ARIMA( 3,0,1) ×( 1,1,1)24 model with estimated variance of 0. 404 9 and AIC of 284. 85 was established. The experimental results show that the seasonal ARIMA model has a short prediction period and a high prediction measuring accuracy,which can effectively predict the operation of water supply network.
In order to reduce the pressure on the secondary water supply facilities to the regional water supply network,we need to accurately predict the operation of the water supply network. By using the technology of the Internet of Things and time series,and through the analysis of the historical data of water supply network,a seasonal ARIMA model is used to predict the data. The data analysis steps and programs were designed,and the ARIMA( 3,0,1) ×( 1,1,1)24 model with estimated variance of 0. 404 9 and AIC of 284. 85 was established. The experimental results show that the seasonal ARIMA model has a short prediction period and a high prediction measuring accuracy,which can effectively predict the operation of water supply network.
引文
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[2]廖曙江,刑佳佳,陈睿迪,等.基于物联网技术的远程建筑消防水压实时监控系统[J].自动化与仪器仪表,2012(5):16-17,20.
[3]付刚,朱晨光,刘彦华.基于远程控制技术的二次供水管理系统的探索与实践[J].中国给水排水,2013,29(12):14-17.
[4]练庭宏,刘秋娟,王景成.基于ARIMA时序辨识的需水量预测[J].控制工程,2008(S1):166-168.
[5]Firat M,Turan M E,Yurdusev M A.Comparative analysis of neural network techniques for predicting water consumption time series[J].Journal of Hydrology,2010,384(1):46-51.
[6]高金良,姚芳,叶健.结合图论的供水管网PMA分区方法[J].哈尔滨工业大学学报,2016,48(8):67-72.
[7]程伟平,赵丹丹,许刚,等.供水管网爆管水力学模型与爆管定位[J].浙江大学学报(工学版),2013,47(6):1057-1062.
[8]Wu Yipeng,Liu Shuming,Wu Xue,et al.Burst detection in district metering areas using a data driven clustering algorithm[J].Water Research,2016,100:28-37.
[9]Hutton C,Kapelan Z.Real-time Burst Detection in Water Distribution Systems Using a Bayesian Demand Forecasting Methodology[J].Procedia Engineering,2015,119(1):13-18.
[10]文玉梅,张雪园,文静,等.依据声信号频率分布和复杂度的供水管道泄漏辨识[J].仪器仪表学报,2014,35(6):1223-1229.
[11]邹柏贤,刘强.基于ARMA模型的网络流量预测[J].计算机研究与发展,2002,39(12):1645-1652.
[12]韩超,宋苏,王成红.基于ARIMA模型的短时交通流实时自适应预测[J].系统仿真学报,2004,16(7):1530-1532,1535.
[13]彭志行,鲍昌俊,赵杨,等.ARIMA乘积季节模型及其在传染病发病预测中的应用[J].数理统计与管理,2008,27(2):362-368.
[14]白云.时间序列特性驱动的供水量预测方法研究及应用[D].重庆大学,2014.