2011
DOI: 10.1007/978-3-642-23957-1_4
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Behavioral Profiles for Building Energy Performance Using eXclusive SOM

Abstract: Abstract. The identification of user and usage profiles in the built environment is of vital importance both for energy performance analysis and smart control purposes. Clustering tools are a suitable means as they are able to discover representative patterns from a myriad of collected data. In this work, the methodology of an eXclusive Self-Organizing Map (XSOM) is proposed as an evolution of a Kohonen map with outlier rejection capabilities. As will be shown, XSOM characteristics fit perfectly with the targe… Show more

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Cited by 2 publications
(2 citation statements)
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“…Ensuring proper hypothesis of prediction, perfect tuning of building services, and functioning of the facility as designed are important factors to monitor the simulation of the building. This has resulted, in the narrowing of the gap between actual energy consumption and estimated energy consumption (Vázquez et al, 2011). Further, Vázquez et al, 2011 mentioned that investigating and creating general models to observe the behaviour of tenants in buildings and using them in energy consumption prediction can reduce variations.…”
Section: Strategies To Reduce the Gap Between Simulation Results And Actual Energy Consumptionmentioning
confidence: 99%
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“…Ensuring proper hypothesis of prediction, perfect tuning of building services, and functioning of the facility as designed are important factors to monitor the simulation of the building. This has resulted, in the narrowing of the gap between actual energy consumption and estimated energy consumption (Vázquez et al, 2011). Further, Vázquez et al, 2011 mentioned that investigating and creating general models to observe the behaviour of tenants in buildings and using them in energy consumption prediction can reduce variations.…”
Section: Strategies To Reduce the Gap Between Simulation Results And Actual Energy Consumptionmentioning
confidence: 99%
“…This has resulted, in the narrowing of the gap between actual energy consumption and estimated energy consumption (Vázquez et al, 2011). Further, Vázquez et al, 2011 mentioned that investigating and creating general models to observe the behaviour of tenants in buildings and using them in energy consumption prediction can reduce variations. Simple observation, obtaining readings, and generating a model to predict energy more absolutely, with the use of basic monitoring results will help to input the energy model with accuracy and predict the actual performance of the building (van den Brom et al, 2018;Gram-Hanssen and Georg, 2018).…”
Section: Strategies To Reduce the Gap Between Simulation Results And Actual Energy Consumptionmentioning
confidence: 99%