{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T03:30:31Z","timestamp":1777519831937,"version":"3.51.4"},"reference-count":25,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,6,30]],"date-time":"2020-06-30T00:00:00Z","timestamp":1593475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006360","name":"Bundesministerium f\u00fcr Wirtschaft und Energie","doi-asserted-by":"publisher","award":["ZF4131804HB8"],"award-info":[{"award-number":["ZF4131804HB8"]}],"id":[{"id":"10.13039\/501100006360","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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\u2019s 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\u2013Hinkley 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.<\/jats:p>","DOI":"10.3390\/e22070731","type":"journal-article","created":{"date-parts":[[2020,6,30]],"date-time":"2020-06-30T16:06:54Z","timestamp":1593533214000},"page":"731","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Entropy-Based Metrics for Occupancy Detection Using Energy Demand"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2750-0502","authenticated-orcid":false,"given":"Denis","family":"Hock","sequence":"first","affiliation":[{"name":"Faculty of Computer Science and Engineering, University of Applied Sciences Frankfurt am Main, 60318 Frankfurt am Main, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Kappes","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Engineering, University of Applied Sciences Frankfurt am Main, 60318 Frankfurt am Main, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1788-547X","authenticated-orcid":false,"given":"Bogdan","family":"Ghita","sequence":"additional","affiliation":[{"name":"School of Engineering, Computing and Mathematics, Plymouth University, Plymouth PL4 8AA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,30]]},"reference":[{"key":"ref_1","first-page":"107","article-title":"Occupancy detection in non-residential buildings\u2014A survey and novel privacy preserved occupancy monitoring solution","volume":"14","author":"Ahmad","year":"2018","journal-title":"Appl. Comput. Inform."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.enbuild.2016.04.008","article-title":"The impact of future scenarios on building refurbishment strategies towards plus energy buildings","volume":"124","author":"Passer","year":"2016","journal-title":"Energy Build."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Vafeiadis, T., Zikos, S., Stavropoulos, G., Ioannidis, D., Krinidis, S., Tzovaras, D., and Moustakas, K. (2017, January 20\u201322). Machine learning based occupancy detection via the use of smart meters. Proceedings of the 1st International Symposium on Computer Science and Intelligent Controls (ISCSIC), Budapest, Hungary.","DOI":"10.1109\/ISCSIC.2017.15"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.enbuild.2015.11.071","article-title":"Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models","volume":"112","author":"Candanedo","year":"2016","journal-title":"Energy Build."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kleiminger, W., Beckel, C., and Santini, S. (2015, January 9\u201311). Household occupancy monitoring using electricity meters. Proceedings of the 3rd International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), Osaka, Japan.","DOI":"10.1145\/2750858.2807538"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3264","DOI":"10.1109\/TMC.2017.2684806","article-title":"Virtual occupancy sensing: Using smart meters to indicate your presence","volume":"16","author":"Jin","year":"2017","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.compenvurbsys.2016.06.003","article-title":"Electricity consumption and household characteristics: Implications for census-taking in a smart metered future","volume":"63","author":"Anderson","year":"2017","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Molina-Markham, A., Shenoy, P., Fu, K., Cecchet, E., and Irwin, D. (2010, January 2). Private memoirs of a smart meter. Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, Zurich, Switzerland.","DOI":"10.1145\/1878431.1878446"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.eneco.2014.07.007","article-title":"Reducing household electricity demand through smart metering: The role of improved information about energy saving","volume":"45","author":"Carroll","year":"2014","journal-title":"Energy Econ."},{"key":"ref_10","unstructured":"McLoughlin, F. (2013). Characterising Domestic Electricity Demand for Customer Load Profile Segmentation. [Ph.D. Thesis, Dublin Institute of Technology]."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kolter, J.Z., and Ferreira, J. (2011, January 7\u201311). A large-scale study on predicting and contextualizing building energy usage. Proceedings of the 25th Conference on Artificial Intelligence (AAAI), San Francisco, CA, USA.","DOI":"10.1609\/aaai.v25i1.7806"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Beckel, C., Sadamori, L., and Santini, S. (2013, January 22\u201324). Automatic socio-economic classification of households using electricity consumption data. Proceedings of the 4th International Conference on Future Energy Systems (e-Energy), Berkeley, CA, USA.","DOI":"10.1145\/2487166.2487175"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1002\/psp.1972","article-title":"The role of digital trace data in supporting the collection of population statistics\u2013the case for smart metered electricity consumption data","volume":"22","author":"Newing","year":"2016","journal-title":"Popul. Space Place"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.enbuild.2012.09.005","article-title":"Energy intelligent buildings based on user activity: A Survey","volume":"56","author":"Nguyen","year":"2013","journal-title":"Energy Build."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.enbuild.2015.02.028","article-title":"Occupancy measurement in commercial office buildings for demand-driven control applications\u2014A survey and detection system evaluation","volume":"93","author":"Labeodan","year":"2015","journal-title":"Energy Build."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s00450-017-0344-9","article-title":"Exploring zero-training algorithms for occupancy detection based on smart meter measurements","volume":"33","author":"Becker","year":"2018","journal-title":"Comput.-Sci.-Res. Dev."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.1016\/j.apenergy.2017.12.002","article-title":"Using machine learning techniques for occupancy-prediction-based cooling control in office buildings","volume":"211","author":"Peng","year":"2018","journal-title":"Appl. Energy"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yan, X., Zhang, L., Li, J., Du, D., and Hou, F. (2020). Entropy-based measures of hypnopompic heart rate variability contribute to the automatic prediction of cardiovascular events. Entropy, 22.","DOI":"10.3390\/e22020241"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Rahimian Boogar, A., Salehi, H., Pourghasemi, H.R., and Blaschke, T. (2019). Predicting habitat suitability and conserving juniperus spp. habitat using SVM and maximum entropy machine learning techniques. Water, 11.","DOI":"10.3390\/w11102049"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kawan, C. (2019). Entropy in networked control. Entropy, 21.","DOI":"10.3390\/e21040392"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/en5010001","article-title":"Mid-term energy demand forecasting by hybrid neuro-fuzzy models","volume":"5","author":"Iranmanesh","year":"2012","journal-title":"Energies"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"74","DOI":"10.3390\/e14010074","article-title":"A new approach to measure volatility in energy markets","volume":"14","author":"Ruiz","year":"2012","journal-title":"Entropy"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"6560","DOI":"10.3390\/e17106560","article-title":"Expected Utility and Entropy-Based Decision-Making Model for Large Consumers in the Smart Grid","volume":"17","author":"Gao","year":"2015","journal-title":"Entropy"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"100290","DOI":"10.1016\/j.segan.2019.100290","article-title":"Using multiple data sources to detect manipulated electricity meter by an entropy-inspired metric","volume":"21","author":"Hock","year":"2020","journal-title":"Sustain. Energy Grids Netw."},{"key":"ref_25","unstructured":"Kendall, M., and Stuart, A. (1983). The Advanced Theory of Statistics, Charles Griffin and Co., Ltd."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/7\/731\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:45:32Z","timestamp":1760175932000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/22\/7\/731"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,30]]},"references-count":25,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,7]]}},"alternative-id":["e22070731"],"URL":"https:\/\/doi.org\/10.3390\/e22070731","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,30]]}}}