1. The impact of occupants’ behaviour on urban energy demand
Frédéric Haldi, Darren Robinson
Ecole Polytechnique Fédérale de Lausanne (EPFL)
Solar Energy and Building Physics Laboratory (LESO-PB)
Vienna, BauSIM 2010 Conference

2. Context
Occupants’ presence and interactions within the built environment are poorly represented in building simulation.
Field observations show that the energy consumption of identical buildings may vary by a factor of two.
The relative influence of occupants’ behaviour is expected to increase in passive buildings.
Recent attempts to model behaviour in building simulation are based on an ‘average occupant’, or on extreme behaviours expressed as best and worse cases.
None of these approaches are likely to result in realistic estimations of buildings’ energy demands.

3. Summary In this research we propose to:
Integrate behavioural models for windows and shading devices, considering both average behaviour and the diversity in individuals’ behaviours.
Rigorously separate the two sources of uncertainity: inter-individual and within-individual variabilities.
Examine the relative weights of building design and occupant behaviour in predicted energy demands, and the interactions between these factors.
Show how to constructively express simulation results as confidence intervals in energy demand.

4. The case study
Simple ‘shoebox’ configuration: volume 44.1 m3, south façade with 40% glazing ratio, boiler with maximum power of 1 kW.
Insulation. Walls U = 0.2 to 0.45 W/(m2K), window U = 1.4 W/(m2K)
Variable parameters:
Openable ratio: 5% and 10%.
Insulation thickness: default thickness or its double.
Setpoint temperature: for heating 18°C and 21°C, for cooling 26°C and 30°C.
25 behavioural profiles.
All possible combinations tested, simulations are repeated 50 times for each variant.

5. Simulation environment
CitySim: simulation tool designed for simulation at the urban scale: a compromise between accuracy and computational overheads.
Sophisticated radiation model
Simplified thermal, HVAC and ECS models
Single-sided ventilation including wind & stack driven air flows.

6. Behavioural models
Window openings and closings are predicted by action probabilities at 5 minute time steps, using indoor and outdoor temperature, rain, previous and next absence durations.
F. Haldi, D. Robinson, Interactions with window openings by office occupants, Building & Environment, 44(12), , 2009.
Actions on shading devices are modelled with lowering action probabilities and distribution of chosen unshaded fraction, using current shaded fraction and indoor and outdoor illuminance.
F. Haldi, D. Robinson, Adaptive actions on shading devices in response to local visual stimuli, Journal of Building Performance Simulation, 3(2), , 2010.
These models show high predictive accuracy and can account for behavioural diversity.

7. Individuals’ diversity
Action probabilities are also examined with respect to individual preferences.
Active and passive occupants are defined on a continuous basis by characteristic temperatures for action.

8. Stochastic variation
The aggregated behavioural profile results in significant variability.
Classical experimental observations: heating and cooling loads may vary by a factor of 2.
Results can be expressed as confidence intervals.

9. Design and setpoint temp.
Expected effects of building design and setpoint temperatures on heating load.
Larger opening areas increase load variability.
Likewise for cooling loads.
Overheating: possibility to define a probability to meet specified requirements.

10. Linear model log(QH) = b0 + bHqH + bOO + bII + bOIOI (R2 = 0.743)
log(QC) = b0 + bCqC + bOO + bII (R2 = 0.854)

11. Behavioural diversity
Implementation of individual profiles gives a more realistic estimation of heating and cooling loads.
The model based on an average occupant predicts a wrong spread.
Failure of deterministic approach (for both magnitude and dispersion).
No evidence for interaction with building design.

12. Diversity and use of controls
Diversity impacts the use of controls, which determines the energy demand.
Heating loads are primarily influenced by window use.
Cooling loads decrease with both shaded fraction and window openings.

13. Mixed-effects model
Variations are mostly explained by individual differences: 60.1% for heating load and 66.1% for cooling load.
Fixed effects (experimental conditions): design, setpoint temp.
Random effects (drawn from a population): individuals.
Mixed-effects models:
log(QH) = b0 + bi + bHqH + bOO + bII + bOIOI
log(QC) = b0 + bi + bCqC + bOO + bII
Inter-individual and
within-individual
variabilities are estimated.

14. Conclusions
Succesful implementation of behavioural models with several levels of complexity (‘average occupant’ or realistic distribution of behavioural patterns).
Study of the influence of design and occupants with their interactions.
Rigorous estimation of the scales of inter-individual and within-individual variabilities.
Better accuracy for building simulation: Fixed deceptive and inexact values are replaced by results such that “in 95% of cases the energy needs will lie in a given range”.
Possibility to test levels of risk, eg. the probability that a target for thermal comfort or energy demand will or will not be met.

15. Contact Frédéric Haldi Darren Robinson
Sustainable Urban Development Group
Solar Energy and Building Physics Laboratory (LESO-PB)
Ecole Polytechnique Fédérale de Lausanne (EPFL)
Lausanne, Switzerland