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Facilitating Deeper Lighting Energy Savings in Open Offices Through Occupancy Controls and Improved Light Switch Locations
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This paper summarizes research conducted to estimate the energy savings from occupancy sensors in open offices, and how those savings vary depending on the number of workstations per occupancy sensor (control group size). The research was conducted by Heschong Mahone Group (HMG) to inform an ongoing ‘Codes and Standards Enhancement’ (CASE) proposal funded by the utility company Southern California Edison (SCE).
A literature review coupled with spreadsheet analysis of field-measured cubicle-level occupancy data formed the basis for the estimate of lighting energy savings from occupancy controls in workgroup sizes of 1,2 and 4 workstations.
Lifecycle costing showed that the proposed measures were cost effective over the 15-year payback period required for the California Energy Efficiency Code.
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Interest in daylight in offices is growing, for both amenity and energy-saving purposes. Low-energy designs often stress good daylight penetration and daylight-linked controls. However, energy consumption estimates are based on guidance derived from research undertaken 10-20 years ago in offices of shallow depth, with simple furnishing plans, and few if any VDUs. Recent surveys suggest that, even with automatic controls, lights in many open-planned offices are on much more than lighting requirements would dictate. Reasons for this are explored, including: poor window design; complex furniture layouts; VDU screen visibility; limited understanding of occupant and management requirements and behaviour; poor implementation and commissioning; misplaced sophistication and poor ergonomics. Office buildings and office work today can also be very different from the contexts in which the original research was done. The paper outlines some general impressions gained from recent case study projects and draws some conclusions for design, management, product development and future research. March 1994 page 1 Daylight use in offices: the opportunities and the fantasies.
Participants (N=40) occupied a glare-free, daylit office laboratory for 1 day, and were prompted every 30 min to use dimming control over electric lighting to choose their preferred light level. Illuminances and luminances were recorded before and after each control opportunity; luminance maps were generated using a calibrated, high-dynamic range digital camera. Although there was a wide variation in chosen light levels between individuals, results showed a significant negative correlation between prevailing desktop illuminance and change in dimmer setting. This indicates that, from the perspective of occupants, daylight does displace electric lighting. Surprisingly, we did not find any luminance-based measure that was as good a predictor of participant dimmer choice as illuminance measured on the desktop. On average, manual dimming control in this situation reduced energy use for lighting by 25% compared to a fixed system delivering 500 Ix of electric lighting on the desktop.
In a major test of different lighting control technologies in a typical office building, we present analyses of seven months' results from five control scenarios in private offices. We compare the energy savings and effectiveness of various combinations of occupant detection, daylight dimming, and switching techniques. Comparing measured energy use with occupant sensors against baseline energy use calculated using wall switch operation only, we found occupant sensors saved 20-26 percent lighting energy compared to manual switching alone. In offices where light sensor controls were: installed and properly commissioned, additional savings up to 27 percent for a total of 46 percent were obtained over a seven-month period, even in an area with unusually high minimum lighting requirements. Dimming the lighting system to desired task levels (task tuning) also resulted in significant (23 percent additional, 43 percent total) energy savings in over lighted areas. On the base case floor, where only bi-level switches were installed, we found significant usage of only one switch resulting in an additional 23 percent savings over single-level switches, an unexpected result with implications for building code requirements. We found the energy savings due to occupant sensing vs. dimming depended on the behavior of occupants. In offices whose occupants tended to stay at their desks all day, dimming controls saved more energy and vice versa. The lighting requirements of occupants appear to depend on their type of work.
This study of open-plan office buildings equipped with locally addressable dimmable lighting relates luminance patterns and levels within the interior to users’ subjective assessments of them. The parts of the field of view which appeared to be related to occupants’ subjective assessments of lighting quality were identified. Luminance measurement was based on digital photography. Occupants on the whole had positive perceptions of lighting quality strongly suggesting that the significant energy savings previously shown to be attributable to systems were not achieved at the expense of occupant comfort.
This paper reports daily and seasonal patterns of lighting use in offices where occupants were able to vary the level of illuminance falling on their working areas. The results show that occupants set a wide range of illuminances, many of which are significantly below CIBSE Code recommendations leading to significant energy savings throughout the year. Although users report use of controls for a variety of reasons, the results of the long-term monitoring of switching behaviour reveals that very few in fact use them for anything other than to switch on upon arrival at work, with further use of systems during the day being rare. Evidence suggests that the way in which systems are configured exerts influence over the level of electric light occupants work under. There is some evidence of an effect of daylight availability on levels set but little evidence to suggest pervasive and consistent user preferences for levels of electric light.
A method is outlined for predicting the likely use of manually operated lighting. The method is based upon patterns of switching behaviour observed in field studies. A series of worked examples are given which demonstrate application of the method to multi-person offices and open-plan teaching spaces. Two points of interest revealed by the examples are the considerable effect that a midday switch-off has on the predicted overall time that lights are on in a space; and the comparatively small effect that gradual afternoon switch-on of lights has on overall use.
An installation in a Federal building tested the effectiveness of a highly-controlled, workstation-specific lighting retrofit. The study took place in an open-office area with 86 cubicles and low levels of daylight. Each cubicle was illuminated by a direct/indirectpendant luminaire with three 32 watt lamps, two dimmable DALI ballasts, and an occupancy sensor. A centralized control system programmed all three lamps to turn on and off according to occupancy on a workstation-by-workstation basis. Field measurements taken over the course of several monthsdemonstrated 40% lighting energy savings compared to a baseline without advanced controls that conforms to GSA's current retrofit standard. A photometric analysis found that the installation provided higher desktop light levels than the baseline, while an occupant survey found that occupants in general preferred the lighting system to thebaseline.Simple payback is fairly high; projects that can achieve lower installation costs and/or higher energy savings and those in which greenhouse gas reduction and occupant satisfaction are significant priorities provide the ideal setting for workstation-specific lighting retrofits.
A study of 14 open-plan offices equipped with occupant controlled general lighting recorded achieved workstation lighting conditions at a number of times of the year. The results confirmed the occurrence of a wide range of workstation lighting levels, many significantly below CIBSE Code recommendations, with average electrical loadings due to lighting in the order of 55% of maximum. System con-figuration had a significant influence on luminaire output, illuminances and energy consumption. In general the lowest luminaire outputs were in buildings where the default reset level was to a low output, with the highest outputs being in buildings having high reset levels and large control groups. In addition locally sited control devices were associated with lower luminaire output.
Studies have been made to discover how people, in their normal working environments, use artificial lighting. A previous paper used the results to assess the cost effectiveness of automatic controls. This paper is concerned with some of the behavioural aspects revealed by the studies, and the results outlined could form a basis for developing a method for predicting the energy consumed by manually operated lighting systems.Time-lapse photography was used to collect the light use data from schools and offices. Observations showed that: 1.1. Usually either all or none of the lighting was in use—it was rare for only part of the lighting to be on.2.2. The cycle of occupation of the space determined when people switched the lights on and off. Lights were switched on (if needed) when people entered a space, but rarely switched off until the space became completely empty. Thus, in continuously occupiedspaces, such as multi-person offices, switching was generally confined to the beginning and end of the normal working day. By contrast, in intermittently occupied spaces, such as school classrooms, switching occurred throughout the day. These distinct patterns of switching activity resulted in quite different profiles of overall lighting use with time of day and daylight availability.3.3. The probability of people switching on the artificial lighting in a daylit space was most closely related to the minimum working plane illuminace; almost as closely related was the average perceived working plane illuminance (calculated from the geometric mean of orientation-corrected daylight factors).
Saving Energy with Highly-Controlled Lighting in an Open-Plan Office
- F Rubenstein
- A Enscoe
Rubenstein, F., Enscoe. A. 2010. Saving Energy with
Highly-Controlled Lighting in an Open-Plan Office.
Leukos, 7 (1).