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Entropy-Based Metrics for Occupancy Detection Using Energy Demand

Faculty of Computer Science and Engineering, University of Applied Sciences Frankfurt am Main, 60318 Frankfurt am Main, Germany
School of Engineering, Computing and Mathematics, Plymouth University, Plymouth PL4 8AA, UK
Author to whom correspondence should be addressed.
Entropy 2020, 22(7), 731;
Received: 16 May 2020 / Revised: 25 June 2020 / Accepted: 29 June 2020 / Published: 30 June 2020
(This article belongs to the Section Information Theory, Probability and Statistics)
Smart Meters provide detailed energy consumption data and rich contextual information that can be utilized to assist electricity providers and consumers in understanding and managing energy use. The detection of human activity in residential households is a valuable extension for applications, such as home automation, demand side management, or non-intrusive load monitoring, but it usually requires the installation of dedicated sensors. In this paper, we propose and evaluate two new metrics, namely the sliding window entropy and the interval entropy, inspired by Shannon’s entropy in order to obtain information regarding human activity from smart meter readings. We emphasise on the application of the entropy and analyse the effect of input parameters, in order to lay the foundation for future work. We compare our method to other methods, including the Page–Hinkley test and geometric moving average, which have been used for occupancy detection on the same dataset by other authors. Our experimental results, using the power measurements of the publicly available ECO dataset, indicate that the accuracy and area under the curve of our method can keep up with other well-known statistical methods, stressing the practical relevance of our approach. View Full-Text
Keywords: energy demand; entropy applications; privacy energy demand; entropy applications; privacy
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Figure 1

  • Externally hosted supplementary file 1
    Doi: 10.5281/zenodo.3830083
    Description: A R-package (tar.gz) and documentation (pdf). The R-package contains all a small part of the labelled groundtruth data as well a method to label the ECO data. All algorithms to detect occupancy are included. The package is build as 'source', so that one can look into the R code. It is possible to easily reproduce most results of our article. Note that, functions to plot, produce tables or compute the optimal threshold are not included. Furthermore, the package is prototypical and not intended to be used productively.
MDPI and ACS Style

Hock, D.; Kappes, M.; Ghita, B. Entropy-Based Metrics for Occupancy Detection Using Energy Demand. Entropy 2020, 22, 731.

AMA Style

Hock D, Kappes M, Ghita B. Entropy-Based Metrics for Occupancy Detection Using Energy Demand. Entropy. 2020; 22(7):731.

Chicago/Turabian Style

Hock, Denis, Martin Kappes, and Bogdan Ghita. 2020. "Entropy-Based Metrics for Occupancy Detection Using Energy Demand" Entropy 22, no. 7: 731.

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