Energy disaggregation for real-time building flexibility detection

E. Mocanu, Ph. Nguyen, M. Gibescu

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

19 Citations (Scopus)
145 Downloads (Pure)


Energy is a limited resource which has to be managed wisely, taking into account both supply-demand matching and capacity constraints in the distribution grid. One aspect of the smart energy management at the building level is given by
the problem of real-time detection of flexible demand available. In this paper we propose the use of energy disaggregation techniques to perform this task. Firstly, we investigate the use of existing classification methods to perform energy disaggregation. A comparison is performed between four classifiers, namely Naive Bayes, k-Nearest Neighbors, Support Vector Machine and AdaBoost. Secondly, we propose the use of Restricted Boltzmann Machine to automatically perform feature extraction. The extracted features are then used as inputs to the four classifiers and consequently shown to improve their accuracy. The efficiency of our approach is demonstrated on a real database consisting of detailed appliance-level measurements with high temporal
resolution, which has been used for energy disaggregation in previous studies, namely the REDD. The results show robustness and good generalization capabilities to newly presented buildings with at least 96% accuracy.
Original languageEnglish
Title of host publication2016 IEEE Power and Energy Society General Meeting, 17-21 July 2016, Boston, Massachusetts
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Publication statusPublished - 14 Nov 2016
Event2016 IEEE Power and Energy Society General Meeting, PESGM 2016 - Sheraton Boston Hotel, Boston, United States
Duration: 17 Jul 201621 Jul 2016


Conference2016 IEEE Power and Energy Society General Meeting, PESGM 2016
Abbreviated titlePESGM 2016
Country/TerritoryUnited States
Internet address


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