Occupant behavior in identical residential buildings: A case study for occupancy profiles extraction and application to building performance simulation

Antonio Muroni,Jan L M Hensen,Pieter-Jan Hoes,Isabella Gaetani

doi:10.1007/s12273-019-0573-x

Antonio Muroni, Jan L M Hensen + Show 2 more

Open Access

https://doi.org/10.1007/s12273-019-0573-x

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Abstract

This study employs a simplified Knowledge Discovery in Database (KDD) to extract occupancy, equipment and light use profiles from a database referred to 12 all-electric prefabricated dwellings in the Netherlands. The profiles are then integrated into a building performance simulation (BPS) model using the software TRNSYS v17. The significance of the extracted profiles is verified by comparing the total and end-use yearly electricity consumption of the investigated dwellings as predicted by the simulation tool with on-site measurements. For the considered dwellings, using standard OB modeling results in an underestimation of the energy use intensity (EUI) by 5.9% to 42.5%, depending on the case. The integration of the occupant behavior (OB) profiles improves the total electricity consumption prediction from an initial 22.9% average deviation from measurements to 1.7%. The results corroborate that the 1.6x discrepancy observed in the buildings’ energy use intensity could be entirely ascribed to OB. Then, the knowledge extracted from the households’ database is used to propose a local electricity market framework to reduce the electricity bill and grid dependency of all households. This study confirms the need for appropriate OB modeling in BPS, it shows the potential of the KDD method for successful OB profiles extraction, and is a first example of data-mined OB profiles integration in BPS, as well as of OB profiles deployment for a practical application other than energy use prediction.

Highlights

In recent years, the importance of occupant behavior (OB) for building energy performance has been widely recognized (Zhang et al 2014; Attia et al 2013; Hensen 2011; Daniel et al 2015)
The results corroborate that the 1.6x discrepancy observed in the buildings’ energy use intensity could be entirely ascribed to OB
This study confirms the need for appropriate OB modeling in building performance simulation (BPS), it shows the potential of the Knowledge Discovery in Database (KDD) method for successful OB profiles extraction, and is a first example of data-mined OB profiles integration in BPS, as well as of OB profiles deployment for a practical application other than energy use prediction

Summary

Introduction

The importance of occupant behavior (OB) for building energy performance has been widely recognized (Zhang et al 2014; Attia et al 2013; Hensen 2011; Daniel et al 2015). The relative impact of OB on building performance is shown to increase as building standards become more stringent and building envelopes and systems more efficient (Hong and Lin 2012; Clevenger and Haymaker 2006). In residential buildings, such influence appears to be even more crucial, due to a higher level of freedom and control over the indoor environment (Urban and Gomez 2013; Andersen 2012; Bahaj and James 2007; Saldanha and BeausoleilMorrison 2012; Gram-Hanseen 2010; Maier et al 2009; Juodis et al 2009). In similar low energy houses (Bahaj and James 2007), different OB patterns led to a dramatic difference in energy consumption, which is shown to reach up to 80% during certain periods of the year

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