Short-term water demand forecasting (StWDF) is the foundation stone in the derivation of an optimal plan for controlling water supply systems. Deep learning (DL) approaches provide the most accurate solutions for this purpose. However, they suffer from complexity problem due to the massive number of parameters, in addition to the high forecasting error at the extreme points. In this work, an effective method to alleviate the error at these points is proposed. It is based on extending the data by inserting virtual data within the actual data to relieve the nonlinearity around them. To our knowledge, this is the first work that considers the problem related to the extreme points. Moreover, the water demand forecasting model proposed in this work is a novel DL model with relatively low complexity. The basic model uses the gated recurrent unit (GRU) to handle the sequential relationship in the historical demand data, while an unsupervised classification method, K-means, is introduced for the creation of new features to enhance the prediction accuracy with less number of parameters. Real data obtained from two different water plants in China are used to train and verify the model proposed. The prediction results and the comparison with the state-of-the-art illustrate that the method proposed reduces the complexity of the model six times of what achieved in the literature while conserving the same accuracy. Furthermore, it is found that extending the data set significantly reduces the error by about 30%. However, it increases the training time.

翻译：短期需水量预测（StWDF）是制定供水系统优化控制方案的基石。深度学习方法为此提供了最精确的解决方案。然而，这些方法因参数数量庞大而存在复杂度问题，且在极值点处预测误差较高。本研究提出了一种有效方法来减轻这些点上的误差。该方法基于通过在实际数据中插入虚拟数据来扩展数据集，以缓解其周围的非线性。据我们所知，这是首个关注极值点相关问题的研究。此外，本文提出的需水量预测模型是一种复杂度相对较低的新型深度学习模型。基础模型使用门控循环单元处理历史需求数据中的序列关系，同时引入无监督分类方法K-means来创建新特征，从而以更少的参数提高预测精度。研究使用来自中国两个不同水厂的实际数据对所提模型进行训练和验证。预测结果及与现有先进方法的比较表明，所提方法在保持相同精度的同时，将模型复杂度降低至文献中已有成果的六分之一。此外，研究发现扩展数据集可将误差显著降低约30%，但会增加训练时间。