Occupant responses to load-shedding events

Rutgers Center for Green Building

Handi Chandra Putra, Clinton Andrews, MaryAnn Sorensen Allacci, Jennifer Senick, Deborah Plotnik Rutgers University, New Brunswick, NJ greenbuilding.rutgers.edu

ABSTRACT Occupant comfort and satisfaction are important considerations in designing buildings. This study incorporates occupant behavior into building information modeling. Using an Agent-Based-Modeling (ABM) approach, the model simulates occupant behavior observed in case study buildings.

METHODS •  Data collected in three LEED certified

multi-tenanted commercial office buildings in the Northeast between 2010 and 2012 that experienced load shedding events—an experimental stimulus.

•  Fieldwork consisted of post-occupancy (POE) evaluations of occupant perceptions and behaviors and building performance evaluations (BPE) documenting energy usage and system functionality.

•  The simulation-modeling framework incorporates the EnergyPlus building energy performance model and drives it using the OpenStudio plug-in to the Sketchup building geometry design tool. The human agent submodel is

•  programmed in the NetLogo agent-based modeling environment, and a connective tissue of Java code links the submodels together into an integrated simulation system. The modeling framework implements a theory of human behavior based on the Belief-Desire-Intention framework (BDI) from artificial intelligence.

OBJECTIVES To investigate opportunities for improving building performance and occupant satisfaction through an iterative process of empirical fieldwork in green buildings and computer simulation modeling for tailoring building design to address the needs of occupants.

MODELING FRAMEWORK BUILDING MODEL AND OCCUPANT MODEL

SIMULATION 2 : BUILDING 2

CONCLUSIONS •  Occupants’ behaviors in Building 1 did

not show dramatic shift during changing indoor air temperature. The story was similar for lighting.

•  Experiments in Building 2 indicated that a cost-minimizing strategy uses less energy and decreases comfort relative to the more typical comfort-maximizing strategy.

•  Comparing both scenarios, the patterns of energy use, temperature, discomfort, and effort are quite similar.

•  Locus-of-control simulations suggest that a majority vote of occupants yields better outcomes than centralized control by the building manager.

FURTHER RESEARCH •  Continue to calibrate and validate for the

future. •  Optimize the algorithm to speed up the

simulation process and allow the occupant behavior module to become an add-in to commercial BIM software.

•  Extend this modeling approach to the domains of interior design and urban design.

REFERENCES •  Andrews, C.J., J.A. Senick, R.E. Wener, and

M. Sorensen Allacci. 2012. Investigating Building Performance through Simulation of Occupant Behavior, Proceedings of GreenBuild 2012, U.S. Green Building Council, 7 pp.

•  Andrews, C.J.; Yi, D.; Krogmann, U.; Senick, J.A.; Wener, R.E. 2011. Designing Buildings for Real Occupants: An Agent-Based Approach, Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on , vol.41, no.6, pp.1077-1091, Nov. 2011.

•  Wilensky, U., & Rand, W. 2013. An introduction to agent-based modeling: Modeling natural, social and engineered complex systems with NetLogo. Cambridge, MA: MIT Press.

ACKNOWLEDGEMENT This material is based upon work supported by the Energy Efficient Buildings Hub (EEB Hub), an energy innovation hub sponsored by the U.S. Department of Energy under Award Number DE-EE0004261 and NSF grant no. 0725503

SIMULATION 1: BUILDING 1 FIELDWORK RESULTS

Building  Data(Architectural  and  

Mechanical  Drawings)

Google  SketchUp Open  Studio

Energy  Plus

Occupant  Behavior  Simulation

Occupant  Behavior  

Survey  Data

Calibration  Analysis

Building  Geometry

HVAC  Construction  Lighting,  Etc

Calibrated  Building  Models

NetLogo

Utility  Bills

What-­‐if  scenarios

Occupancy Schedule: •  Number of People (num)

Thermal Adaptive Schedule: •  Temperature SP (oC) •  Local Heater (num) •  Local Fan (on/off) •  Temperature SP (Load

Shedding) (oC)

Lighting Adaptive Schedule: •  Overhead Light (on/off) •  Task Light (num) •  Windows Blinds (open/close)

Loca-tion

ID

Work hours

Temp Diff

Environment

Effort

Discomfort

Cost Light Diff

E+ Parameters Agents Attributes

TWO CASE STUDIES

Building 1 Building 2

Perception and Temperature Behavior

Perception and Temperature Behavior, Shed Days

Energy Usage (kwh) Discomfort Level

Cost Minimizing Comfort Maximizing

Locus of Control: Energy (kwh) and Occupant Discomfort

Load Shedding Load Shedding: Temperature (Site 1 and Site 2)

Comparison of Load Shed Effects, Site 1 and 2: Satisfaction with Environmental Conditions

Light Preferences Thermal Preferences