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A platform for research: civil engineering, architecture and urbanism
Impact of the pre-simulation process of occupant behaviour modelling for residential energy demand simulations
Occupant behaviour models play an important role in building energy demand modelling. Useful simulation algorithms have been developed in previous studies; however, the pre-simulation process to prepare modelling parameters for simulated occupants has received less attention. This study elaborated on the pre-simulation process and evaluated how it may alter model performance. We selected the activity-starting probability using American time use survey data as an example. The model performance was compared under three cases representing different numbers and types of variables together with three parameter preparation methods: multinomial log-linear regression, support vector machine, and artificial neural network. All the methods considering basic demographic and time-related variables performed well in reproducing the average probabilities. An increase in significant variables contributed to the reproduction of inter-occupant diversity. All the methods showed similar performances within the given dataset, although they were practically different. The results offer practical guidance for shaping the pre-simulation process.
Impact of the pre-simulation process of occupant behaviour modelling for residential energy demand simulations
Occupant behaviour models play an important role in building energy demand modelling. Useful simulation algorithms have been developed in previous studies; however, the pre-simulation process to prepare modelling parameters for simulated occupants has received less attention. This study elaborated on the pre-simulation process and evaluated how it may alter model performance. We selected the activity-starting probability using American time use survey data as an example. The model performance was compared under three cases representing different numbers and types of variables together with three parameter preparation methods: multinomial log-linear regression, support vector machine, and artificial neural network. All the methods considering basic demographic and time-related variables performed well in reproducing the average probabilities. An increase in significant variables contributed to the reproduction of inter-occupant diversity. All the methods showed similar performances within the given dataset, although they were practically different. The results offer practical guidance for shaping the pre-simulation process.
Impact of the pre-simulation process of occupant behaviour modelling for residential energy demand simulations
Li, Yuanmeng (author) / Yamaguchi, Yohei (author) / Shimoda, Yoshiyuki (author)
Journal of Building Performance Simulation ; 15 ; 287-306
2022-05-04
20 pages
Article (Journal)
Electronic Resource
Unknown
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