Evaluation of the Performance Gains in Short-Term Water Consumption Forecasting by Feature Engineering via a Fuzzy Clustering Algorithm in the Context of Data Scarcity

Abstract

Accurate short-term water consumption forecasting is a crucial function of modern water supply systems, which, in turn, play a crucial role in the sustainable management of water resources, particularly in regions with limited access to water supplies. This study presents an evaluation of the performance gains in short-term water consumption forecasting by the exploitation of a fuzzy clustering algorithm to engineer new features corresponding to water consumption clusters. The evaluation takes place under data scarcity, meaning both a small dataset and only in situ water consumption measurements. To evaluate the gains, data registered to consumers on the remote island of Tilos are processed to produce two datasets which differ in terms of the addition of clusters. The datasets are consumed by deep neural networks to produce hour-ahead predictions. The inclusion of the clusters in the dataset results in a decreased mean absolute error and root-mean-square error, reduced by 29% and 17% on average, respectively.

Keywords

• deep neural networks
• short-term forecasting
• water consumption