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Accurate Household Occupant Behavior Modeling Based on Data Mining Techniques


Abstract An important requirement of household energy simulation models is their accuracy in estimating energy demand and its fluctuations. Occupant behavior has a major impact upon energy demand. However, Markov chains, the traditional approach to model occupant behavior, (1) has limitations in accurately capturing the coordinated behavior of occupants and (2) is prone to over-fitting. To address these issues, we propose a novel approach that relies on a combination of data mining techniques. The core idea of our model is to determine the behavior of occupants based on nearest neighbor comparison over a database of sample data. Importantly, the model takes into account features related to the coordination of occupants’ activities. We use a customized distance function suited for mixed categorical and numerical data. Further, association rule learning allows us to capture the coordination between occupants. Using real data from four households in Japan we are able to show that our model outperforms the traditional Markov chain model with respect to occupant coordination and generalization of behavior patterns.
Year 2014
Keywords Multi-Agent Societies;
Authors Márcia Baptista, Anjie Fang, Helmut Prendinger, Rui Prada, Yohei Yamaguchi
Booktitle AAAI-14 - 28th AAAI Conference on Artificial Intelligence
Volume 3166/2004
Pages 1164-1170
Organization AAAI
Publisher AAAI
Address Quebec, Canada
Month July
Pdf File \"pdf
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@inproceedings { baptista14, abstract = {An important requirement of household energy simulation models is their accuracy in estimating energy demand and its fluctuations. Occupant behavior has a major impact upon energy demand. However, Markov chains, the traditional approach to model occupant behavior, (1) has limitations in accurately capturing the coordinated behavior of occupants and (2) is prone to over-fitting. To address these issues, we propose a novel approach that relies on a combination of data mining techniques. The core idea of our model is to determine the behavior of occupants based on nearest neighbor comparison over a database of sample data. Importantly, the model takes into account features related to the coordination of occupants’ activities. We use a customized distance function suited for mixed categorical and numerical data. Further, association rule learning allows us to capture the coordination between occupants. Using real data from four households in Japan we are able to show that our model outperforms the traditional Markov chain model with respect to occupant coordination and generalization of behavior patterns.}, address = { Quebec, Canada}, booktitle = {AAAI-14 - 28th AAAI Conference on Artificial Intelligence}, keywords = {Multi-Agent Societies;}, month = {July}, pages = {1164-1170}, publisher = {AAAI}, title = {Accurate Household Occupant Behavior Modeling Based on Data Mining Techniques}, volume = {3166/2004}, year = {2014}, author = {Márcia Baptista and Anjie Fang and Helmut Prendinger and Rui Prada and Yohei Yamaguchi} }

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