Regression Tree (RT) forecasting models are widely used in short-term demand forecasting. Likewise, Self-Organizing Maps (SOM) models are known for their ability to cluster and organize unlabeled big data. Herein, a combination of these two Machine Learning (ML) techniques is proposed and compared to a standalone RT and a Seasonal Autoregressive Integrated Moving Average (SARIMA) models, in forecasting the short-term water demand of a municipality. The inclusion of the Unsupervised Machine Learning clustering model has resulted in a significant improvement in the performance of the Supervised Machine Learning forecasting model. The results show that using the output of the SOM clustering model as an input for the RT forecasting model can, on average, double the accuracy of water demand forecasting. The Mean Absolute Percentage Error (MAPE) and the Normalized Root Mean Squared Error (NRMSE) were calculated for the proposed models forecasting 1 h, 8 h, 24 h, and 7 days ahead. The results show that the hybrid models outperformed the standalone RT model, and the broadly used SARIMA model. On average, hybrid models achieved double accuracy in all 4 forecast periodicities. The increase in forecasting accuracy afforded by this hybridized modeling approach is encouraging. In our application, it shows promises for more efficient energy and water management at the water utilities.

中文翻译：

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使用混合有监督和无监督机器学习模型的短期需水量预测

回归树（RT）预测模型广泛用于短期需求预测中。同样，自组织地图（SOM）模型以聚类和组织未标记大数据的能力而闻名。在此，提出了这两种机器学习（ML）技术的组合，并将其与独立RT和季节性自回归综合移动平均线（SARIMA）模型进行比较，以预测市政当局的短期需水量。纳入无监督机器学习群集模型已导致对有监督机器学习预测模型的性能进行了重大改进。结果表明，将SOM聚类模型的输出用作RT预测模型的输入，平均而言，可以使需水量预测的准确性提高一倍。为预测的1小时，8小时，24小时和7天前的拟议模型计算了平均绝对百分比误差（MAPE）和归一化均方根误差（NRMSE）。结果表明，混合模型的性能优于独立RT模型和广泛使用的SARIMA模型。平均而言，混合模型在所有4个预测周期中均实现了双精度。这种混合建模方法所带来的预测准确性的提高令人鼓舞。在我们的应用程序中，它显示了在自来水公司提高能源和水管理效率的希望。混合模型在所有4个预测周期中均实现了双精度。这种混合建模方法所带来的预测准确性的提高令人鼓舞。在我们的应用程序中，它显示了在自来水公司提高能源和水管理效率的希望。混合模型在所有4个预测周期中均实现了双精度。这种混合建模方法所带来的预测准确性的提高令人鼓舞。在我们的应用程序中，它显示了在自来水公司提高能源和水管理效率的希望。