Circle fitting by linear and nonlinear least squares
Journal of Optimization Theory and Applications
A Space-Economical Suffix Tree Construction Algorithm
Journal of the ACM (JACM)
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Annotating smart environment sensor data for activity learning
Technology and Health Care - Smart Environments: Technology to Support Healthcare
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Human Activity Recognition and Pattern Discovery
IEEE Pervasive Computing
Studying always-on electricity feedback in the home
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the 12th ACM international conference on Ubiquitous computing
Towards an understanding of campus-scale power consumption
Proceedings of the Third ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Reduce the Number of Sensors: Sensing Acoustic Emissions to Estimate Appliance Energy Usage
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
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Society is becoming increasingly aware of the impact that our lifestyle choices make on energy usage and the environment. As a result, research attention is being directed toward green technology, environmentally-friendly building designs, and smart grids. This paper looks at the user side of sustainability. In particular, it looks at energy consumption in everyday home environments to examine the relationship between behavioral patterns and energy consumption. It first demonstrates how data mining techniques may be used to find patterns and anomalies in smart home-based energy data. Next, it describes a method to correlate home-based activities with electricity usage. Finally, it describes how this information could inform users about their personal energy consumption and to support activities in a more energy-efficient manner. These approaches are validated by using real energy data collected in a set of smart home testbeds.