On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Cramming more components onto integrated circuits
Readings in computer architecture
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Temporal Machine Learning for Switching Control
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Recognizing End-User Transactions in Performance Management
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
An empirical comparison of supervised learning algorithms
ICML '06 Proceedings of the 23rd international conference on Machine learning
Proceedings of the 2008 ACM conference on Computer supported cooperative work
Technology-Enabled Feedback on Domestic Energy Consumption: Articulating a Set of Design Concerns
IEEE Pervasive Computing
The design of eco-feedback technology
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
ICT for green: how computers can help us to conserve energy
Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking
Social devices: autonomous artifacts that communicate on the internet
IOT'08 Proceedings of the 1st international conference on The internet of things
Sustainable energy practices at work: understanding the role of workers in energy conservation
Proceedings of the 6th Nordic Conference on Human-Computer Interaction: Extending Boundaries
Fighting against Vampire Appliances through Eco-Aware Things
IMIS '12 Proceedings of the 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing
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Society wastes much more energy than it should. This produces tons of unnecessary CO_2 emissions. This is partly due to the inadequate use of electrical devices given the intangible and invisible nature of energy. This misuse of devices and energy unawareness is particularly relevant in public spaces offices, schools, hospitals and so on, where people use electrical appliances, but they do not directly pay the invoice to energy providers. Embedding intelligence within public, shared appliances, transforming them into Eco-aware things, is valuable to reduce a proportion of the unnecessarily consumed energy. To this end, we present a twofold approach for better energy efficiency in public spaces: 1 informing persuasively to concerned users about the misuse of electronic appliances; 2 Customizing the operating mode of this everyday electrical appliances as a function of their real usage pattern. To back this approach, a capsule-based coffee machine placed in a research laboratory has been augmented. This device is able to continuously collect its usage pattern to offer feedback to coffee consumers about the energy wasting and also, to intelligently adapt its operation to reduce wasted energy. To this aim, several machine learning approaches are compared and evaluated to forecast the next-day device usage.