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End-use Classification using High-Resolution Smart Water Meter Data

Type: MA thesis

Status: running

Date: May 1, 2020 - November 30, 2020

Supervisors: Andreas Maier, Siming Bayer

Smart water meters are widely used for billing of water consumption. Real-time data acquired with smart water meters provide new opportunities for the utility companies to create an intelligent and efficient water distribution network aimed at reducing costs and non-revenue losses. However, various factors such as degree of limescale, water quality or flow affect the accuracy of the measurements of a smart meter device greatly, especially the accumulated long-term influences of the aforementioned external factors result in significant non-conformance in the measurements. Hence, a predictive error estimation of the smart meter measurements and, thus, the degree of erosion of a device benefits the overall maintenance process from the meter level to the distribution network level.

In order to estimate the measurement error in a forehanded manner, end-use consumption patterns [1 – 3] (e.g. use of dishwasher etc.) can first be classified and extracted from the smart meter data, i.e. real-time measurements in a water distribution network. This step is the so called data disaggregation. Subsequently, the variance of the measurement accuracy is determined by comparing the results of disaggregation with reference measurements (e.g. same consumption pattern recognized from historical data). Therefore, a highly accurate classification of end-uses is fundamental for a precise estimation of the condition of a smart meter on the sensor level predictively.
In this work, we are focusing on the classification of the end-use consumption patterns using high-resolution smart meter data, especially for water distribution networks. The thesis consists of the following aspects:

  1. Literature review of water event clustering and water end-use classification techniques
  2. Analysis and understanding of the existing data
  3. Development and implementation of a water end-use classification framework
  4. Evaluation of the implemented approach
    1. Feature extraction
    2. Clustering of water events
    3. Classification of established water end-uses

[1] Mario Vašak, Goran Banjac, and Hrvoje Novak. Water use disaggregation based on classification of feature vectors extracted from smart meter data. Procedia Engineering, 119(1):1381–1390, 2015.

[2] Khoi Anh Nguyen, Rodney A. Stewart, and Hong Zhang. An autonomous and intelligent expert system for residential water end-use classification. Expert Systems with Applications, 41(2):342–356, 2014.

[3] L. Pastor-Jabaloyes, F. J. Arregui, and R. Cobacho. Water end use disaggregation based on soft computing techniques. Water, 10(1):321–341, 2018.