Analyze the impact of lighting control strategies

Example BEM analysis results showing the impact of daylight controls

BEM can be used to quantify the potential impact of automatic lighting controls.  

See also test strategies to reduce lighting power density.

Impact of lighting control strategies

Lighting controls will have several related impacts: lighting energy consumption, internal heat gain and HVAC energy consumption. These impacts are discussed in more detail in this topic: test strategies to reduce lighting power density,

Occupant behavior will have a big impact on the magnitude of the savings provided by lighting controls. For example, the savings for occupancy sensors will depend on the patterns of space usage. Savings for automatic daylighting controls will be affected in many cases by occupant control of interior blinds. For these reasons, any estimate of savings for lighting controls will be subject to uncertainty.

Alternatives for lighting control strategies

Results of a meta analysis on studies of lighting control savings. Shows average, minimum and maximum savings along with the sample size for each strategy.

Each of these lighting control strategies is intended to save energy by automatically turning off or dimming electric lighting when the light is not needed or is in excess of what is needed. Due to the potential for significant energy savings, building energy codes require specific automatic lighting controls in some types of spaces. So it is a good idea to identify for your project which controls are required and which can be considered for “beyond code” energy savings.

Time-of-day control

A traditional lighting control strategy, and one that is often required by energy codes, is to automatically turn lights on and off with time-based controls, i.e. a “time clock”. These controls reduce after-hours lighting energy consumption. A manual override feature is often provided to allow occupants to turn lights on for a specified period when space is occupied outside of the normally scheduled time. It is not common to claim energy savings for time-of-day control because it is considered either a code requirement or standard practice.

Occupancy-based control

Motion sensors are commonly used to shut off lights when a space, or portion of a space, is unoccupied. These controls may also be required by energy codes in some types of spaces, such as enclosed offices, that are expected to have intermittent occupancy. Extra savings might be achieved by using occupancy control in additional spaces.

Daylight control

In spaces that receive daylight, automatic daylight controls reduce the use of electric lighting when daylight is available. These automatic controls use one or more photocells to detect the level of daylight and then either dim the output of electric lights or switch them off, sometimes in steps, to maintain a target illuminance. Savings provided by daylighting controls will be enhanced with effective fenestration design (see fenestration and daylighting options). And energy codes may require daylighting control to be installed in some spaces.  

Tuning

Modern dimming controls, which are readily available for LED lighting systems, provide the opportunity to tune ( i.e. reduce) illuminance to match the needs of occupants. With tuning controls, lights operate at reduced output whenever they are on.

There are several reasons why a lighting system may be installed that provides more light than necessary. First, lighting designers need to account for the reduction of light output over the life of a lamp, so the initial illuminance may be higher than the design target. In other cases, especially in smaller spaces, the fact that lighting fixtures are available only with discrete levels of light output may lead to a design with illuminance above target. And individuals have different preferences regarding light levels (and require more light as they age), so the same design that is comfortable for some may feel too bright to others. In each of these cases, there may be opportunity for energy savings with tuning control.

Personal tuning. This strategy adjusts light levels according to the personal preferences of occupants. It applies to spaces like private offices, workstation-specific lighting in open-plan offices, and classrooms.

Institutional tuning. This strategy adjusts maximum light levels for all or part of a building to provide a specific illuminance level. In some cases, the space usage is not known for certain before the building is built and occupied. A tuning strategy allows the lights to be adjusted to provide appropriate illuminance for the actual space usage, and this adjustment might apply to a whole building or just a portion of the building with unique usage.

Guidance on modeling approach

There are a few approaches commonly used to represent the impact of lighting controls in building energy models.

• Modified lighting schedule. In this case, the lighting schedule is adjusted to represent expected control behavior, such as a schedule that varies lighting usage hour-by-hour to represent the expected impact of occupancy control or that varies lighting to represent the expected impact of daylight control.
• Lighting power adjustment. This is a common and simple approach to represent the impact of lighting controls is to reduce the input for lighting power by a percentage based on the type of control. This approach is used in some energy codes to provide credit for lighting controls.
• Daylight calculation. This approach uses BEM software capability to estimate changing daylight illuminance within a space and adjust electric lighting output accordingly.

Occupancy-based control modeling

Probably the most common approach to representing occupancy-based control in energy models is the lighting power adjustment method. The choice of an appropriate value for percent savings requires judgment and is subject to significant uncertainty. The following table lists data from sources that provide values for consideration. Be aware that if you are looking to use the model for energy code compliance, be sure to check the rules for required lighting controls to determine where credit is allowed for occupancy-based control savings.

Savings due to occupancy-based control from two sources

Building Type	Meta Analysis (note 1)	ASHRAE 90.1-2022 Appendix G Table G3.7-1 (note 2)
Office	22% (n=23)	30% enclosed 15% open plan 25% corridor 45% restroom
Warehouse	31% (n=4)	45% storage area
Lodging	45% (n=2)	45% guest room
Education	18% (n=5)	30% classroom
Retail (other than mall)		15% sales area
Healthcare inpatient		10% exam, patient room, nurse station 25% corridor 45% supply
Public assembly	36% (n=2)	25% lobby 35% dining 10% auditorium, theater
Healthcare outpatient	23% (n=1)	10% exam 25% corridor 45% supply
Other	7% (n=1)

Note 1: This column lists average savings based on a meta analysis of lighting control studies. Sample size indicated for each case.

Note 2: This column includes examples of occupancy-control savings fractions listed in Table G3.7-1 of ASHRAE Standard 90.1-2022, which are based on comparison to the lighting control requirements of the 2004 version of the standard. Occupancy sensor control in many of these spaces will be required by the mandatory requirements in the 2022 standard.

Daylight control modeling

There are two commonly used methods to represent daylight control in building energy models, and each has its pros and cons. The first is typically faster while the second has the potential to be more accurate. The first method is generally adequate for evaluating the savings potential of automatic daylight control and test the impact of design features such as window area and glazing properties. The second method may be more appropriate when comparing detailed daylighting design alternatives.

BEM software daylight calculation. Many BEM tools have the capability to calculate daylight illuminance in spaces with windows or skylights and will calculate the reduction in lighting power for each timestep by comparing the available daylight to a specified illuminance target. The typical inputs for each daylighted space include the following:

• Daylight sensor location
• Illuminance target (footcandles or lux)
• Fraction of lighting power in the space that is controlled
• Type of control (dimming, stepped, on/off).

There are several other inputs that affect daylight calculations.

• Fenestration design: window area, window location and visible light transmittance of the glazing
• Visible reflectance of interior surfaces
• Adjacent buildings, trees or landscape features
• Ground reflectance
• Movable interior shades and associated control inputs.

It is important to understand any limitations of the daylight calculation method used in your BEM tool. Many BEM tools use the split-flux method for daylight calculations. This is a simplified approach that is reasonably accurate for basic space and window geometries. However, this method may not accurately represent the impact of complex fenestration configurations such as light shelves or light-redirecting blinds which are intended to increase the penetration of daylight.

Daylight analysis software. In this second method, modified lighting schedules are created using daylight analysis software to calculate daylight illuminance at different times of the day and year for representative spaces. The process of creating these schedules may be done manually or may be automated by applications that couple BEM software and daylight software. These daylight analysis programs use more accurate radiosity or ray-tracing calculation methods. The benefit to this approach is potentially more accurate daylight illuminance calculations for non-standard spaces and for designs with light-redirecting devices such as light shelves. The drawback is that significant extra time, both modeler time and computer calculation time, may be required to perform the separate daylight analysis.

Tuning control modeling

The potential savings due to tuning control may be represented in a BEM model with a reduction in lighting power or an adjusted lighting schedule. The appropriate savings assumption will vary depending on the situation and will require judgment.

A meta analysis of lighting studies found that the average savings were 31% for personal tuning and 36% for institutional tuning.

The California Building Energy Efficiency Standards offer credit for institutional tuning of 10% in non-daylit areas and 5% in daylit areas.

Model quality check

A model quality check can help identify model input errors.

• Check end-use results for expected changes in lighting energy consumption and associated changes in heating and cooling energy.
• For daylighting controls, review output reports that provide summaries of simulated daylight illuminance values and associated lighting power reductions.

See also review and analysis to verify model quality.

Guidance on presenting results

When presenting savings results for lighting controls, it is recommended to include a discussion of the uncertainty related to occupant behavior. Presentation of a likely range of potential savings may be appropriate.