Short-term water demand forecasting has always been a hot research topic in the field of water distribution systems, and many researchers have developed a wide variety of methods based on different prediction periodicities. However, few studies have paid attention to using ensemble learning methods for short-term water demand forecasting. In this study, an ensemble-learning-based method was developed to forecast short-term water demand. The proposed method consists of two models: an equal-dimension and new-information model and an ensemble learning model. The purpose of the equal-dimension and new-information model is to update data for modelling periodically, while the ensemble learning model is used for water demand forecasting. To evaluate the forecasting performance, the proposed method was applied to a data set obtained from a real-world water distribution system and compared with the single back-propagation neural network (BPNN) model and the seasonal autoregressive integrated moving average (SARIMA) model. The results show that the proposed method improves both the accuracy and stability of water demand prediction. The proposed method has the potential to provide a promising alternative for short-term water demand forecasting.

短期需水量预测一直是配水系统领域的研究热点，许多研究人员根据不同的预测周期开发了各种各样的方法。但是，很少有研究将集成学习方法用于短期需水量预测。在这项研究中，开发了一种基于集成学习的方法来预测短期需水量。所提出的方法包括两个模型：等维和新信息模型以及集成学习模型。等维新信息模型的目的是定期更新数据以进行建模，而集成学习模型用于需水预测。为了评估预测效果，将该方法应用于从实际供水系统获得的数据集，并与单向传播神经网络（BPNN）模型和季节性自回归综合移动平均（SARIMA）模型进行了比较。结果表明，该方法提高了需水量预测的准确性和稳定性。所提出的方法有可能为短期需水量预测提供有希望的替代方法。