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Technol."],"published-print":{"date-parts":[[2018,5,31]]},"abstract":"<jats:p>Energy disaggregation, the task of taking a whole home electricity signal and decomposing it into its component appliances, has been proved to be essential in energy conservation research. One powerful cue for breaking down the entire household\u2019s energy consumption is user\u2019s daily energy usage behavior, which has so far received little attention: existing works on energy disaggregation mostly ignored the relationship between the energy usages of various appliances by householders across different time slots. The major challenge in modeling such a relationship in that, with ambiguous appliance usage membership of householders, we find it difficult to appropriately model the influence between appliances, since such influence is determined by human behaviors in energy usage. To address this problem, we propose to model the influence between householders\u2019 energy usage behaviors directly through a novel probabilistic model, which combines topic models with the Hawkes processes. The proposed model simultaneously disaggregates the whole home electricity signal into each component appliance and infers the appliance usage membership of household members and enables those two tasks to mutually benefit each other. Experimental results on both synthetic data and four real-world data sets demonstrate the effectiveness of our model, which outperforms state-of-the-art approaches in not only decomposing the entire consumed energy to each appliance in houses but also the inference of household structures. We further analyze the inferred appliance-householder assignment and the corresponding influence within the appliance usage of each householder and across different householders, which provides insight into appealing human behavior patterns in appliance usage.<\/jats:p>","DOI":"10.1145\/3108413","type":"journal-article","created":{"date-parts":[[2018,1,29]],"date-time":"2018-01-29T13:48:05Z","timestamp":1517233685000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Energy Usage Behavior Modeling in Energy Disaggregation via Hawkes Processes"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2883-7529","authenticated-orcid":false,"given":"Liangda","family":"Li","sequence":"first","affiliation":[{"name":"Yahoo Research, Sunnyvale, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyuan","family":"Zha","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, GA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,1,29]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfineco.2015.03.002"},{"volume-title":"Proceedings of the 28th AAAI Conference on Artificial Intelligence. 1164--1170","author":"Baptista M.","key":"e_1_2_1_2_1"},{"volume-title":"Proceedings of the 2012 Workshop on Data Mining Applications in Sustainability.","author":"Barker S.","key":"e_1_2_1_3_1"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1214\/06-BA104"},{"key":"e_1_2_1_5_1","unstructured":"D. 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