TY - JOUR
T1 - Occupant behavior in identical residential buildings
T2 - a case study for occupancy profiles extraction and application to building performance simulation
AU - Muroni, Antonio
AU - Gaetani dell'Aquila d'Aragona, Isabella
AU - Hoes, Pieter-Jan
AU - Hensen, Jan
PY - 2019/12/1
Y1 - 2019/12/1
N2 - 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.
AB - 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.
KW - Occupant behavior
KW - occupant behavior profiles
KW - data-mining
KW - identical dwellings
UR - http://www.scopus.com/inward/record.url?scp=85071881189&partnerID=8YFLogxK
U2 - 10.1007/s12273-019-0573-x
DO - 10.1007/s12273-019-0573-x
M3 - Article
VL - 12
SP - 1047
EP - 1061
JO - Building Simulation
JF - Building Simulation
SN - 1996-3599
IS - 6
ER -