数据驱动的城镇智慧水务日用水量预测算法

Daily Water Volume Prediction Algorithm of Urban Smart Water Based on Big Data

针对国内某中小型自来水公司的实际供水情况,通过对比相关系数分析了天气等因素对日供水量的影响,确定了日用水量预测所需的输入参数;比较了3种用传统基于大数据的水量预测方法在该自来水公司中应用的性能,针对用传统方法预测误差较大的问题,提出引入前一日用水量和前8 h的用水量作为影响因素的改进方法。将所提方法在该自来水公司的信息系统中进行了实际测试,验证了所提算法的有效性。根据算法性能和实现复杂度,给出了适用于城镇水务的水量预测算法和算法执行形式,能够帮助水务企业提高水量预测精度,有效提升水资源的利用率。

According to the actual water supply situation of a small and medium-sized water company in China,the influences of weather and other factors on daily water supply are analyzed by comparing the correlation coefficient,so as to determine the input parameters required for daily water consumption prediction.The application performance of three traditional water volume prediction methods is compared using the actual operating data.To solve the severe errors existing in the traditional methods,an improved method is proposed,which takes the water consumption of the previous day and 8 hours into consideration.The efficiency of the proposed algorithm is verified by the tests in the information system of the water supply company.According to the performance and implementation complexity of the algorithm,a water quantity prediction algorithm and its suitable implementation form for urban water affairs are proposed,which can help the water affair system improve the water quantity prediction accuracy,thus effectively improving the utilization rate of water resources.