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Office Building Plug Load Disaggregation

BETTER BUILDINGS ALLIANCE

Background

Plug loads account for a significant and growing portion 

of the energy consumed in commercial buildings, but 

they are one of the most difficult end uses to manage.

Typically, building owners and managers do not have

effective methods for monitoring plug load energy 

consumption. Sometimes plug loads are wired to a 

dedicated circuit, such that they can be metered in 

aggregate at the panel level. While this is helpful for 

evaluating plug load energy consumption at a building 

level, to truly understand how and when specific types 

of devices are consuming energy, metering must be 

done at the device level.

Today, smart plugs can meter and control devices and 

wirelessly report energy consumption to a central plug 

load management system. Smart plugs offer the 

potential for full building granular plug load monitoring. 

However, with thousands of devices in today’s large 

buildings, individually monitoring every plug load 

becomes a nontrivial task. Researchers at the National 

Renewable Energy Laboratory (NREL) have attempted 

to address this issue by proposing a method for 

combining a limited amount of smart plug metering with 

a device inventory to develop a disaggregated 

breakdown of device-level power consumption in a zero 

energy office building.1

Disaggregation Study

Three months of power data were collected from 118 

devices (15 types) in NREL’s Research Support Facility

(RSF) using Intellisocket smart plugs from Ibis 

Networks. An inventory of the devices in the RSF B Wing 

East was also conducted and used to estimate the 

number of devices of each type in the wing. Scaling the 

power consumption data by the estimated number of 

devices allowed the researchers to develop a 

disaggregated plug load profile for the wing. The plug 

loads in each wing of the RSF are wired to individual 

submeters so the researchers could compare the 

disaggregated model to the wing’s measured aggregate 

plug loads. They found the disaggregated model’s 

shape was similar to that of the plug load submeter, but 

the magnitude of the model was less than the submeter, 

indicating there were likely devices contributing to the 

submeter that were not captured by the model.

Figure 1. NREL’s Research Support Facility. Image 

courtesy of Dennis Schroeder.

Figure 2. Disaggregated plug load breakdown for an 

average workday in the B Wing East.

1 These findings were published in: Doherty, B. & Trenbath, K. (2019). Device-level plug load disaggregation in a zero energy office building and opportunities for 

energy savings. Energy and Buildings, 203, 109480.

KEY TAKEAWAYS

 Taking a device inventory can lead to a 

better understanding of the variety and 

quantity of devices in a building.

 Combining a device inventory with a limited 

metering effort can reveal a building’s 

disaggregated plug load profile and identify 

devices using more energy than expected.

 Disaggregation enables comparison of 

device consumption during occupied and 

unoccupied hours for better targeted controls 

and energy efficiency upgrades.

 The devices in a building evolve over time 

and plug load management strategies must 

evolve to meet these changes.
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Findings & Insights

The disaggregated plug load model (Fig 2) 

revealed new insights, including:

 Laptops and monitors account for more than 

20% of the daytime load, but only a small 

portion of the evening load.

 Occupant preference can significantly affect 

plug load profile shape. The B Wing East has a

midday spike to microwaves during lunch, while 

other studies find midday dips in buildings where 

occupants typically leave for lunch.

 Audiovisual (AV) controllers and central 

monitoring station (CMS) TVs (on 24/7 for 

security) together contribute, on average, 

more than 3.5 kW to the baseload.

Occupied and Unoccupied Loads

The device-level power data can be divided by 

occupied times and unoccupied times to better 

identify opportunities for energy savings (Fig 4). 

 Video conference cameras, desktop servers, 

AV controllers and CMS TVs make up a larger 

portion of the unoccupied load than the 

occupied load.

 The unoccupied load could be reduced by 

25% by adding controls to only 13 devices and 

shutting them off during unoccupied times. These 

devices included AV controllers, video 

conference cameras, and copiers.

Capturing Device Usage Diversity

For devices with load profiles that are dependent 

on usage, it is important to meter enough 

instances of these devices to capture their usage 

diversity. The morning spike due to the coffee 

makers in Fig 2 is a result of metering only one 

coffee maker instance and not capturing the 

usage diversity across the devices in the wing.

Device Load Profile Comparison

Collecting device-level power consumption data 

allows for comparison of device load profiles. Fig 

3 demonstrates that the laptops and monitors 

are most strongly correlated with each other and 

with the plug load submeter. The coffee makers, 

water boilers, and microwaves are also positively 

correlated as they tend to be used in the morning 

and at lunchtime.

Figure 3. (Left) Scatterplots demonstrating the relationship 

between the plug load submeter, desktop server, and monitors. 

(Right) Graphic of Spearman rank correlation coefficients. Larger 

circles indicate stronger correlation.

Figure 4. Mean power consumption as a percentage of the mean 

total plug load power in the B Wing East during occupied times (9

a.m. to 5 p.m.) and unoccupied times (9 p.m. to 5 a.m.). Purple 

indicates metered data and yellow indicates estimated data.

Conclusion

This study demonstrated that a device inventory and a 

limited device-level metering effort can produce a 

disaggregated plug load breakdown, uncovering

energy savings opportunities. This study is limited to 

the RSF, however, and should be validated in other 

buildings to see if the method is generally effective.