严格水资源管理制度实施的背景下，短期用水量预测对城市供水系统调度的作用日益显著。在分析日用水量时序演化规律及随机性影响因素的基础上，以前7天每日用水量、日最高温度、当月用水量占全年比、日降水量、节假情况作为短期用水量预测指标，构建了BP神经网络城市短期用水量预测模型，并利用贝叶斯正则化对BP神经网络进行优化。将两种模型应用于广州市某自来水公司进行对比验证，结果表明，贝叶斯神经网络预测模型与BP神经网络预测模型的平均绝对百分比误差分别达0.87%与1.85%，经贝叶斯正则化的BP神经网络模型泛化能力更强，精度提高了约0.98%，更符合城市短期用水量预测的高精度要求。

Under the background of implementation of the most stringent management regulations on water resources, the prediction of short-term water consumption is playing an increasingly significant role in urban water supply system scheduling. Based on the analysis of the temporal evolution pattern and random factors of short-term water consumption, a Bayesian neural network prediction model for urban short-term water consumption was built with the daily maximum temperature, daily water consumption of the previous 7 days, ratio of water consumption of the current month to the annual amount, daily precipitation, and holidays as predictors of short-term water consumption. Meanwhile, Bayesian regularization was used to optimize BP neural network. Both BP network model and the optimized model were applied to a running-water company in Guangzhou City for tesing. The results indicated that the mean absolute percentage error of the Bayesian neural network prediction model was 0.87%, while that of the BP neural network prediction model was 1.85%. Compared to the BP neural network prediction model, the optimized model has stronger generalization ability, with accuracy improved by about 0.98%. Thus, it fits better with the high-precision requirement of urban short-term water prediction.

国家自然科学基金 (U150120175)