The user side of sustainability: Modeling behavior and energy usage in the home

Chen, Chao; Cook, Diane J.; Crandall, Aaron S.

doi:10.1016/j.pmcj.2012.10.004

Cited by 53 publications

(29 citation statements)

References 18 publications

(13 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…One of the datasets of the Center for Advanced Studies in Adaptive Systems (CASAS) project [15], for instance, contains readings from a power meter; however they provide information only about the overall consumption, which is not very useful in the context of activity recognition, where fine-grained energy monitoring is needed. Namely, aggregated information about energy consumption often leads to non optimal consumption control.…”

Section: Related Workmentioning

confidence: 99%

User activity recognition for energy saving in smart homes

Cottone

Gaglio

et al. 2015

Pervasive and Mobile Computing

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

User activity recognition for energy saving in smart homes

Cottone

Gaglio

et al. 2015

Pervasive and Mobile Computing

View full text Add to dashboard Cite

“…The CASAS project of the Washington State University is a typical example of such an approach [4], but there exists numerous similar project over the world (cf. [5] for example). Basically, such approaches are confronted with the mismatch between a priori models and raw data provided by sensors (cf.…”

Section: Related Workmentioning

confidence: 99%

Emergence of Regularities in the Stochastic Behavior of Human

Goc

Barthelot

Pascual³

2015

2015 IEEE International Conference on Data Mining Workshop (ICDMW)

View full text Add to dashboard Cite

This paper aims to show that regularities emerge from the strongly stochastic properties of human behavior. To this aim, the paper presents the application of a new Knowledge Discovery in Database (KDD) process, called Timed Observations Mining for Learning (TOM4L), on the timed data provided by a smart building of offices of the southeast of France during 12 months from April 2011 to March 2012. The TOM4L process produces then 12 behavioral models of the white-collar workers of the office, one for each month of the studied period. This sequence of models put on the light the strongly stochastic properties of human behavior since they differ significantly from one month to another. This illustrates the intrinsic difficulty of discovering behavior rules from the timed data provided by a smart environment. Nevertheless, regularities clearly emerges from this sequence of behavioral models that are closely linked with seasons. Two seasons, a cold season of five month and a warm seasons of seven months, are clearly identified with this sequence but to this aim, more abstract models are required. Finally, this paper shows that the TOM4L approach is clearly operational and powerful for human behavior modeling in smart environments but higher abstraction levels of representation must be defined to discover more general behavior rules.

show abstract

“…By combining these information, the system marks the energy consumption of a certain device as a wasteful energy consumption if no user was in the proximity of this device during the time the device was turned on. In [7], Chen et al developed CASAS Sustain System which has the goals of detecting anomalies in house energy consumption and relating this consumption to the user different behavioral patterns. The authors apply data mining techniques on the energy consumption historical data in order to detect certain anomalies in energy consumption.…”

Section: Related Workmentioning

confidence: 99%

“…Compared to [13] and [7], our work does not put any analysis burden on the user side. In [13] and [7], the user has to look at the data analysis results and find out himself if he was wasting energy.…”

Section: Related Workmentioning

confidence: 99%

SMARTENERGY.KOM: An intelligent system for energy saving in smart home

Alhamoud

Ruettiger

Reinhardt

et al. 2014

39th Annual IEEE Conference on Local Computer Networks Workshops

View full text Add to dashboard Cite

Over the last twenty years, energy conservation has always been of great importance to individuals, societies and decision makers around the globe. As a result, IT researchers have shown a great interest in providing efficient, reliable and easy-to-use IT services which help users saving energy at home by making use of the current advances in Information and Communications Technology (ICT). Driven by the aforementioned motivation, we developed SMARTENERGY.KOM, our framework for realizing energy efficient smart homes based on wireless sensor networks and human activity detection. Our work is based on the idea that most of the user activities at home are related to a set of electrical appliances which are necessary to perform these activities. Therefore, we show how it is possible to detect the user's current activity by monitoring his fine-grained appliance-level energy consumption. This relation between activities and electrical appliances makes it possible to detect appliances which could be wasting energy at home. Our framework is organized in two components. On one hand, the activity detection framework which is responsible for detecting the user's current activity based on his energy consumption. On the other hand, the EnergyAdvisor framework which utilizes the activity detection for the purpose of recognizing the appliances which are wasting energy at home and informing the user about optimization potential.

show abstract

The user side of sustainability: Modeling behavior and energy usage in the home

Cited by 53 publications

References 18 publications

User activity recognition for energy saving in smart homes

User activity recognition for energy saving in smart homes

Emergence of Regularities in the Stochastic Behavior of Human

SMARTENERGY.KOM: An intelligent system for energy saving in smart home

Contact Info

Product

Resources

About