A collaborative framework for annotating energy datasets

Abstract

Targeting human activities responsible for the energy consumption instead of focusing solely on single appliance feedback for achieving energy efficiency in residential homes would link human behaviors to the resulting energy consumption. To this end, learning when appliances are in an active or idle state and the related user activity is crucial. Until smart appliances become widespread and can communicate their internal state, identifying when the residents interact with the appliances has to be determined from the available information that can be recorded from these devices. Developing and validating learning models require ground truth in the form of annotations to indicate when an appliance is active or idle. Launching data collection campaigns to incorporate these missing ground truth data involves careful planning before the roll-out of the experiment. Prohibitive costs for the hardware and time investment to monitor the deployed equipment are necessary for quality data. As such, publicly released datasets containing appliance-level data offer a basis for most researchers. This paper addresses these challenges by providing a collaborative web-based framework to retrofit labeling on existing datasets. The platform is publicly available, applies the wisdom of the crowd in the realm of energy research and leverages gamification techniques to encourage users' active contribution. The access to the platform and furthermore to the expert manually labeled dataset intends to enable future research and foster more collaboration in this area.

Gamified crowdsourcing: Conceptualization, literature review, and future agenda

“…Y. Lee et al, 2013;Y. Liu, Alexandrova, Nakajima et al, 2011;Machnik et al, 2015;Martella et al, 2015;Massung et al, 2013;Melenhorst et al, 2015;Nose and Hishiyama, 2013;Packham and Suleman, 2015;Pothineni et al, 2014;Prandi et al, 2016;Preist et al, 2014;Prestopnik and Tang, 2015;Roengsamut et al, 2015;Runge et al, 2015;Saito et al, 2014;Simões and De Amicis, 2016;Snijders et al, 2015;Sørensen et al, 2016;Talasila et al, 2016;Tinati et al, 2016;Vasilescu et al, 2014 37 33.6 Empirical studies with no results on how gamification works in crowdsourcing Bentzien et al, 2013;Brenner et al, 2014;Brito et al, 2015;Cao et al, 2015;Chamberlain, 2014;Cucari et al, 2016;Deng et al, 2016;Dos Santos et al, 2015;Harris, 2014;He et al, 2014;Inaba et al, 2015;Kacorri et al, 2014;Kurita et al, 2016;Lauto and Valentin, 2016;Lessel et al, 2015;Mason et al, 2012;Nagai et al, 2014;Nunzio et al, 2016;Riegler et al, 2015;…”

“…Bentzien et al, 2013;Bowser et al, 2013;Cao et al, 2015;Chamberlain, 2014;Cucari et al, 2016; De Franga et al, 2015; Dergousoff and Mandryk, 2015; Dumitrache et al, 2013; Goncalves et al, 2014; He et al, 2014; Itoko et al, 2014; Kacorri et al, 2014, 2015; Kobayashi et al, 2015; Kurita et al, 2016; Lauto and Valentin, 2016; J. J. Lee et al, 2013; T. Y. Lee et al, 2013; Lessel et al, 2015; Y. Liu, Alexandrova, Nakajima et al, 2011; Martella et al, 2015; Mason et al, 2012; Nagai et al, 2014; Nose and Hishiyama, 2013; Nunzio et al, 2016; Pothineni et al, 2014; Prestopnik and Tang, 2015; Roengsamut et al, 2015; Rosani et al, 2015; Runge et al, 2015; Saito et al, 2014; Sakamoto and Nakajima, 2014; Sheng, 2013; Simões and De Amicis, 2016;Snijders et al, 2015;Sørensen et al, 2016;Tinati et al, 2016;Ustalov, 2015;Uzun et al, 2013;Vasilescu et al, 2014;Yakushin and Lee, 2014; Yu et al., 2014;Brito et al, 2015;Choi et al, 2014;Deng et al, 2016;Dos Santos et al, 2015;Harris, 2014;Inaba et al, 2015;Kawajiri et alrefer to studies in which empirical results about gamification have been reported.…”

Simple and effective descriptive analysis of missing data anomalies in smart home energy consumption readings

“…All the literature works related to data analytics in the smart home application are segregated into general works [12]- [26] and works related to specific cases. To understand these specific cases, this paper investigates the research conducted on real-time datasets, viz., U.S.A. based Pecan Street power consumption dataset [27]- [35], Germany and Australia based Tracebase dataset [36]- [63], etc. Table 1, Table 2, and Table 3 elucidate all these state-of-the-art literature work respectively in terms of research works done on general energy consumption data as well as specific datasets.…”

Performance evaluation in non‐intrusive load monitoring: Datasets, metrics, and tools—A review

“…On the other hand, a fully manual system would require an outstanding level of resources and still be prone to errors. Against this background, we believe that future work should look at ways of leveraging the potential of the existing data, for example by means of collaborative and semi-automatic annotation of datasets (Cao, Wijaya, Aberer, & Nunes, 2015;Pereira & Nunes, 2015), or even the creation of synthetic datasets by means of data synthesizing (Buneeva & Reinhardt, 2017;Henriet, Simsekli, Fuentes, & Richard, 2018).…”