4 research outputs found
Energy saving in smart homes based on consumer behaviour: A case study
This paper presents a case study of a recommender system that can be used to
save energy in smart homes without lowering the comfort of the inhabitants. We
present an algorithm that uses consumer behavior data only and uses machine
learning to suggest actions for inhabitants to reduce the energy consumption of
their homes. The system mines for frequent and periodic patterns in the event
data provided by the Digitalstrom home automation system. These patterns are
converted into association rules, prioritized and compared with the current
behavior of the inhabitants. If the system detects an opportunities to save
energy without decreasing the comfort level it sends a recommendation to the
residents.Comment: To be presented on IEEE International Smart Cities Conference 201
Generating Time-Based Label Refinements to Discover More Precise Process Models
Process mining is a research field focused on the analysis of event data with
the aim of extracting insights related to dynamic behavior. Applying process
mining techniques on data from smart home environments has the potential to
provide valuable insights in (un)healthy habits and to contribute to ambient
assisted living solutions. Finding the right event labels to enable the
application of process mining techniques is however far from trivial, as simply
using the triggering sensor as the label for sensor events results in
uninformative models that allow for too much behavior (overgeneralizing).
Refinements of sensor level event labels suggested by domain experts have been
shown to enable discovery of more precise and insightful process models.
However, there exists no automated approach to generate refinements of event
labels in the context of process mining. In this paper we propose a framework
for the automated generation of label refinements based on the time attribute
of events, allowing us to distinguish behaviourally different instances of the
same event type based on their time attribute. We show on a case study with
real life smart home event data that using automatically generated refined
labels in process discovery, we can find more specific, and therefore more
insightful, process models. We observe that one label refinement could have an
effect on the usefulness of other label refinements when used together.
Therefore, we explore four strategies to generate useful combinations of
multiple label refinements and evaluate those on three real life smart home
event logs
Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey
Future buildings will offer new convenience, comfort, and efficiency
possibilities to their residents. Changes will occur to the way people live as
technology involves into people's lives and information processing is fully
integrated into their daily living activities and objects. The future
expectation of smart buildings includes making the residents' experience as
easy and comfortable as possible. The massive streaming data generated and
captured by smart building appliances and devices contains valuable information
that needs to be mined to facilitate timely actions and better decision making.
Machine learning and big data analytics will undoubtedly play a critical role
to enable the delivery of such smart services. In this paper, we survey the
area of smart building with a special focus on the role of techniques from
machine learning and big data analytics. This survey also reviews the current
trends and challenges faced in the development of smart building services
Using Association Rule Mining to Discover Temporal Relations of Daily Activities
Abstract. The increasing aging population has inspired many machine learning researchers to find innovative solutions for assisted living. A problem often encountered in assisted living settings is activity recognition. Although activity recognition has been vastly studied by many researchers, the temporal features that constitute an activity usually have been ignored by researchers. Temporal features can provide useful insights for building predictive activity models and for recognizing activities. In this paper, we explore the use of temporal features for activity recognition in assisted living settings. We discover temporal relations such as order of activities, as well as their corresponding start time and duration features. To validate our method, we used four months of real data collected from a smart home