Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
NextPlace: a spatio-temporal prediction framework for pervasive systems
Pervasive'11 Proceedings of the 9th international conference on Pervasive computing
Agent-based control for decentralised demand side management in the smart grid
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Review: Bayesian networks in environmental modelling
Environmental Modelling & Software
A unified framework for modeling and predicting going-out behavior
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
Understanding domestic energy consumption through interactive visualisation: a field study
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Improving location prediction services for new users with probabilistic latent semantic analysis
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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We address the problem of forecasting the usage of multiple electrical appliances by domestic users, with the aim of providing suggestions about the best time to run appliances in order to reduce carbon emissions and save money (assuming time-of-use pricing), while minimising the impact on the users' daily habits. An important challenge related to this problem is the modelling the everyday routine of the consumers and of the interdependencies between the use of different appliances. Given this, we develop an important building block of future home energy management systems: a prediction algorithm, based on a graphical model, that captures the everyday habits and the inter-dependency between appliances by exploiting their periodic features. We demonstrate through extensive empirical evaluations on real-world data from a prominent database that our approach outperforms existing methods by up to 47%.