Investigating Water Consumption Patterns Through Time Series Clustering

Abstract

Due to the rapid population growth and economic development, water management became a necessity to assure sustainability. Analyzing water consumption patterns enables decision makers to better manage resources to meet the current demands without compromising future needs. This research paper focuses on investigating water consumption patterns through time series clustering in Dubai, United Arab Emirates, as one of the major water-stressed cities. Agglomerative hierarchical was applied to cluster the consumption patterns into multiple groups based on behavioral similarities. The consumption behavior of each cluster is analyzed based on the residential, commercial, and industrial sectors from 2017 to 2020. The study resulted in classifying the datasets into five clusters, in which the residential sector had the highest consumption, followed by the commercial and industrial sectors. Moreover, the majority of clusters reported high water consumption in 2020, except for clusters with relatively low number of accounts. In addition, the effect of data clustering was investigated in order to improve water demand forecasting. Based on the results, time series clustering increased the accuracy of predictions, in which artificial neural network and random forest models obtained the highest accuracy specially in clusters with high number of observations. It was found that the forecasting performance was highly correlated to the number of communities in each cluster.