Smart meters installed at the user-level provide a new data source for managing water infrastructure. This research explores the use of machine learning methods, including Random Forests (RFs), Artificial Neural Networks (ANNs), and Support Vector Regression (SVR) to forecast hourly water demand at 90 accounts using smart-metered data. Demands are predicted using lagged demand, seasonality, weather, and household characteristics. Time-series clustering is applied to delineate data based on the time of day and days of the week, which improves model performance. Two modeling approaches are compared. Individual models are developed separately for each meter, and a Group model is trained using a data set of multiple meters. Individual models predict demands at meters in the original data set with lower error than Group models, while the Group model predicts demands at new meters with lower error than Individual models. Results demonstrate that RF and ANN perform better than SVR across all scenarios.

在用户级别安装的智能仪表为管理水基础设施提供了新的数据源。这项研究探索了使用机器学习方法（包括随机森林（RF），人工神经网络（ANN）和支持向量回归（SVR））来使用智能计量数据来预测90个账户的每小时需水量。使用滞后的需求，季节性，天气和家庭特征来预测需求。应用时间序列聚类以基于一天中的时间和一周中的几天来描绘数据，从而提高了模型性能。比较了两种建模方法。为每个仪表单独开发单独的模型，并使用多个仪表的数据集训练组模型。个别模型可以在原始数据集中预测电表的需求，而误差要低于组模型，组模型预测新电表的需求，其误差要低于单个模型。结果表明，在所有情况下，RF和ANN的性能均优于SVR。