Data Mining to Uncover Heterogeneous Water Use Behaviors From Smart Meter Data

Knowledge on the determinants and patterns of water demand for different consumers supports the design of customized demand management strategies. Smart meters coupled with big data analytics tools create a unique opportunity to support such strategies. Yet, at present, the information content of smart meter data is not fully mined and usually needs to be complemented with water fixture inventory and survey data to achieve detailed customer segmentation based on end use water usage. In this paper, we developed a data‐driven approach that extracts information on heterogeneous water end use routines, main end use components, and temporal characteristics, only via data mining existing smart meter readings at the scale of individual households. We tested our approach on data from 327 households in Australia, each monitored with smart meters logging water use readings every 5 s. As part of the approach, we first disaggregated the household‐level water use time series into different end uses via Autoflow. We then adapted a customer segmentation based on eigenbehavior analysis to discriminate among heterogeneous water end use routines and identify clusters of consumers presenting similar routines. Results revealed three main water end use profile clusters, each characterized by a primary end use: shower, clothes washing, and irrigation. Time‐of‐use and intensity‐of‐use differences exist within each class, as well as different characteristics of regularity and periodicity over time. Our customer segmentation analysis approach provides utilities with a concise snapshot of recurrent water use routines from smart meter data and can be used to support customized demand management strategies.TU Berlin, Open-Access-Mittel - 201

Improving feature extraction via time series modeling for structural health monitoring based on unsupervised learning methods

Feature extraction by time series modeling based on statistical pattern recognition is a powerful approach to Structural Health Monitoring (SHM). Determination of an adequate order and identi cation of an appropriate model play prominent roles in extracting sensitive features to damage from time series representations. Early damage detection under statistical decision-making via high-dimensional features is another signi cant issue. The main objectives of this study were to improve a residual-based feature extraction method by time series modeling and to propose a multivariate data visualization approach to early damage detection. A simple graphical tool based on Box-Jenkins methodology was adopted to identify the most compatible time series model with vibration time-domain measurements. Furthermore, k-means and Gaussian Mixture Model (GMM) clustering techniques were utilized to examine the performance of the residuals of the identi ed model in damage detection. A numerical concrete beam and an experimental benchmark model were applied to verifying the improved and proposed methods along with comparative analyses. Results showed that the approaches were successful and superior to a state-of-the-art order determination technique in obtaining a sufficient order, generating uncorrelated residuals, extracting sensitive features to damage, and accurately detecting early damage by high-dimensional data