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Monitoring user behavior : monitoring and analysis of manual control
strategies for lighting and blinds
Reinhart, C. F.; Wienold, J.
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Monitoring user behaviour: monitoring and analysis of manual
control strategies for lighting and blinds
Reinhart, C.F.
NRCC-44749
A version of this document is published in / Une version de ce document se trouve dans :
International Daylighting : RD & A, no. 2, April 2001, pp. 6-7
Monitoring User Behavior
Monitoring and Analysis of Manual Control Strategies for Lighting and Blinds
Christoph F. Reinhart, Institute for Research in Construction, National Research Council Canada (christoph.reinhart@nrc.ca) Jan Wienold, Fraunhofer Institute for Solar Energy Systems (wienold@ise.fhg.de)
So far, most building simulation models tend to ”treat people like fixed metabolic heat generators passively experiencing the indoor environment” (Newsham 94). These models neglect the fact that office users do actually influence their immediate working environment by operating the artificial lighting system and/or the shading and glare protection device for example. The exact nature of this user-building interaction may significantly influence the incoming solar gains and therefore the thermal and visual comfort conditions as well as cooling and heating loads. The influence is pronounced in buildings with large architectural glazings.
This paper presents the experimental setup and preliminary results of a pilot study which aims to gain more insight into the degree and kind of manual control strategies of external blinds and artificial lighting systems which are currently practiced in office buildings.
Experimental Setup
Figure 1 shows the investigated building which is situated in Weilheim near Stuttgart, Germany. The building has been occupied since the early spring of 2000 and hosts the land surveying company
Lamparter GbR. It consists of two rows of offices
with 10 offices on each side facing SSW and NNE. Only the Southern offices were considered in this study. As no active air-conditioning system has been installed, the offices rely on a careful management of incoming solar gains in the Summer (passive cooling). The building is part of the German SolarBau:MONITOR Project (Voss 2000).
Figure 1(b) provides a sketch of the daylighting concept. The artificial lighting and the external venetian blinds are connected to an EIB (European Installation Bus) building control system. The artificial lighting is provided by two purely indirect dimmable luminaires, each with 2x58 W
lamps (11.6 Wm-2).
Figure 1 - Monitored office building Lamparter
While the artificial lighting system is manually switched on and off, lighting levels are automatically dimmed via a ceiling mounted illuminance sensor which is connected to a closed-loop light level control system. The external two-component blinds system acts as a combined heat and glare protection device and is supported by an external lightshelf which shades the occupants from direct sunlight and redirects daylight deeper into the room. Manual blind control is possible at all times, and any manual blind manipulation disables the automated blind control for 2 hours. When active, the control system fully lowers/retracts the blinds if the illuminance onto the Southern facade exceeds/falls below 40,000 lx. This control algorithm has been chosen to avoid overheating in times of temporarily absence.
The data collection included the recording of work place occupancy, work plane illuminances, indoor and ambient temperatures, direct and diffuse irradiances, the illumiance onto the Southern façade as well as the status of the external blinds and lighting. All these qunatities were recorded with a time resolution of 15 minutes or lower.
office communication area
light shelf
indirect lighting
Figure 2 - Daylighting concept in the offices
Manual Control of Artificial Lighting
According to Love, manual artificial lighting control strategies can be assigned to either of the following behavioral patterns:
(I) people who switch the lights for the duration of the working day and keep it on even in times of temporary absence
(II) people who use electric lighting only when indoor illuminance levels due to daylight are low For the latter class Hunt established his well-known correlation between the times of switching of electric lighting systems and the extremes of a period of occupation. Switching mainly takes place when entering or vacating a space, and the
switch-on probability upswitch-on arrival exhibits a strswitch-ong
correlation with minimum daylight illuminances in the working area. Hunt’s original correlation is the solid line depicted in Figure 3 while the dashed line shows the relationship for 8 out to 10 offices from the present study. Clearly, a relationship similar to Hunt’s could be derived from the data from these offices. Absolute illuminance levels are different in both study as because different subjects have been observed, and because the minimum work plane illuminance level is a somewhat arbitrary quantity which strongly depends on the actual considered work places. The manual control strategies in office 2 and 4 were not considered in Figure 3 as these users tend to belong to Love’s behavioral class (I), i.e. they often switched the lighting upon arrival in the morning and kept the lighting activated throughout the working day. Whereas all users switched the lighting off at the end of the working day, switching off at lunch times did occur regularly but not always.
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 100 200 300 400 500 600
minimum work plane illumiance [lux]
Switch On Probability [%]
Hunt Lamparter
Figure 3 - comparison of the switch on probabilities found by Hunt and in this study. Only offices in which the occupants could be assigned to the Love’s behavioral class (II) were considered.
Artificial Lighting – Blinds interaction
The combination of the external, 2-component venetian blinds with the external light shelf has been chosen to admit sufficient daylight so that artificial lighting could be avoided at times of substantial ambient daylight illuminances. Figure 4 shows, for all 10 offices, the positions of the venetian blinds for the times when the artificial lighting was switched on. It is clearly visible, that for the majority of hours the blinds were fully retracted whenever the lighting was activated. This finding implies that the utilized daylight strategy successfully reduced the artificial lighting demand in most offices.
Figure 4 – percentage of occupied times when the artificial lighting was activated and when the venetian blinds were either fully refracted or not.
Further Research
This article presents a snapshot of the data analysis which is presently under work. A future work package is to develop a manual control model which predicts annual electric energy demands for
artificial lighting based on user occupancy, indoor illuminance levels as well as a limited number input parameters which describe the user. This work will be carried out at the National Research Council based on the already available data.
So far it has been tested in how far user occupancy at the work place can be predicted by a stochastic model. User occupancy is an important and independent input variable which uniquely defines the times when a manual manipulation of the lighting or the blinds can occur. Therefore, the basic approach of an existing stochastic user occupancy model, LIGHTSWITCH, (Newsham 1995), has been adapted for the present study to test whether the observed user occupancy profiles can be reproduced. In its present state, model inputs are measured probability profiles for the times of arrival, departure as well as length and times of lunch breaks and intermediate absences from the work place. Figure 5 shows that such detailed information very well reproduce individual user occupancy. The next step will be to replace the detailed, measured model inputs with a few intuitive input parameters.
Figure 5 – observed and monitored occupancy probabilities on weekdays in an example office. Note the lunch break around noon and that the user occupancy model successfully reproduces the measured occupancy pattern.
References
Hunt D.R.G., The use of artificial lighting in relation to daylight levels and occupancy, Bldg. Envir. 14 pp. 21 - 33 (1979) Love J. A., Manual switching patterns observed in private offices, Lighting Res. Technol. 30(1) 45-50 (1998)
Newsham G., Lightswitch: A stochastic model for predicting office lighting energy consumption, Proc. Right Light Three, 3rd European Conference on Energy Efficient Lighting, pp. 59 -66, Newcastle UK (1995)
Newsham G., Manual Control of Window Blinds and Electric Lighting: Implications for Comfort and Energy Consumption, Indoor Environment 3 135 - 144 (1994)
Reinhart C., Daylight Availability and Switching Patterns in Office Buildings – Simulation Studies and User Monitoring,
submitted in 2001
Voss K., Energieeffizienz und Solarenergienutzung im Nichtwohnungsbau – Demonstrationsprojekte, SolarBau:MONITOR, Journal 2000 or http://www.solarbau.de
