Device-level plug load disaggregation in a zero energy office building and opportunities for energy savings

Energy & Buildings 204 (2019) 109480

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Device-level plug load disaggregation in a zero energy office building and opportunities for energy savings Bennett Doherty∗, Kim Trenbath National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401, United States

a r t i c l e

i n f o

Article history: Received 1 May 2019 Revised 26 September 2019 Accepted 30 September 2019 Available online 2 October 2019 Keywords: Energy Efficiency Energy Metering Load Disaggregation Miscellaneous Electric Loads Office Buildings Plug Loads Smart Plugs Zero Energy

a b s t r a c t Along with heating, cooling, ventilation, and lighting, plug loads are one of the principal consumers of energy in commercial buildings. Managing ever-changing plug loads is a significant challenge given the quantity and variety of devices in commercial buildings and the cost of monitoring equipment. To address this issue, we propose a method for developing a disaggregated model of an office building’s plug loads that utilizes power data from a small portion of monitored devices and a device inventory. Using data from metering and control devices in the Research Support Facility at the National Renewable Energy Laboratory, we compared our model to the building’s plug load submeters. We found that the model was effective in predicting the shape of the building’s average plug loads; however, it did not account for the entire magnitude of the load. With the disaggregated breakdown, we identified devices that contributed significantly to the building’s unoccupied load, such as the audio visual equipment as well as devices that contributed significantly at specific times of the day, such as the microwaves at noon. This disaggregated information allows building owners to make more informed decisions with respect to plug load controls and energy efficiency upgrades. In addition, we highlight how the plug loads in the Research Support Facility have changed over time and offer recommendations for implementing this disaggregation method in other buildings. © 2019 Elsevier B.V. All rights reserved.

1. Introduction Commercial buildings are responsible for nearly one-fifth of the primary energy consumed in the United States, and 40 percent of that energy is consumed by plug and process loads (PPLs) [1,2]. PPLs include all plugged-in electronic devices, as well as hardwired devices that are not associated with other major building end uses such as heating, cooling, ventilation, and lighting. As these end uses become more efficient and as devices become even more prevalent in commercial buildings, PPLs are expected to account for a growing percentage of building energy consumption [2]. Despite their significant energy consumption, we often have minimal access to information for managing these loads. At best, a well-submetered building will have high-level information about its plug load consumption (Fig. 1), but even then, we do not know how specific devices consume electricity across durations of time (i.e., daily, weekly, monthly, and annually). Having access to information that breaks down a building’s plug loads into how and when specific types of devices consume electricity would allow a

∗

Corresponding author. E-mail addresses: [email protected] (B. Doherty), [email protected] (K. Trenbath). https://doi.org/10.1016/j.enbuild.2019.109480 0378-7788/© 2019 Elsevier B.V. All rights reserved.

building manager to understand which devices to target for energy savings. Monitoring all PPLs in a commercial building to access this information is a costly endeavor, given the sheer number and variety of devices in large buildings as well as the high cost of metering equipment. We propose a more manageable method for monitoring a small subset of devices in an office building to develop a disaggregated breakdown of the building’s plug loads and to better understand the load profiles of specific device types. Having access to a disaggregated breakdown of a building’s plug loads is an integral step to identifying and quantifying energy savings opportunities [3]. This study attempts to enhance the body of work surrounding plug load efficiency strategies by posing the following research questions: •

•

How can individual device monitoring and building-level submeters be used to develop a disaggregated breakdown of plug loads in an office building? What insights can be gained from having a disaggregated model of plug loads?

To address these questions, we investigated plug loads associated with a section of the Research Support Facility (RSF) at the National Renewable Energy Laboratory (NREL). The study was specific to plug loads commonly found in office buildings, such as laptops, projectors, and coffee makers. Process loads refer to equip-

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B. Doherty and K. Trenbath / Energy & Buildings 204 (2019) 109480

Fig. 1. Research Support Facility energy consumption by end use in October, November, and December 2017. Energy related to hot and chilled water for the mechanical systems is not included in this chart.

ment for commercial or industrial processes, such as vertical transportation, and are often grouped with plug loads, however, they were not included in this study [2]. We used plug load metering and control devices, or “smart plugs,” to monitor the power consumption of 118 individual devices in the building. By combining the power consumption data with a device inventory, we developed a disaggregated model of the plug loads for one wing of the building. Taking advantage of the building’s plug load submeters, we compared our model to the measured plug loads to identify the successes and shortcomings of our approach. The disaggregated model allowed us to observe how specific types of devices contribute to the wing’s plug load demand at various points in the typical workday. We utilized the disaggregated information to uncover device-level insights and highlight areas for plug load energy reductions that likely would have gone unnoticed without the advantage of disaggregation. Additionally, through conversations with the building manager and audio visual (AV) technology professional, we examined how the policies surrounding plug load management have changed since the building was first occupied. Finally, we examined the lessons learned from this study and offer recommendations for researchers or building managers seeking to employ disaggregation using a similar methodology. 2. Background During the last two decades, interest in plug load energy consumption and management has increased as building energy efficiency has improved and plug loads now account for a growing percentage of commercial building energy usage [2]. As a result, building standards such as ASHRAE 90.1 (Section 8.4.2) [3] and California Title 24 (Part 6, Section 130.5) [4] are accelerating the adoption of plug load management strategies by requiring a portion of receptacles within buildings to be controlled. The existing research in plug load management has broad coverage, from metering studies that make use of smart plug technologies for monitoring individual devices to intervention studies that control plug loads remotely or employ behavioral strategies to modify occupants’ consumption patterns. Other studies focus on nonintrusive load monitoring (NILM) techniques for plug load disaggregation, as well as improved plug load modeling strategies based on benchmark values, load profile data sets, and real-time building data. Although

plug load research extends far beyond these topics, the following is a brief summary of the research areas most relevant to this paper. There have been several studies that utilize smart plug technology to monitor the energy consumption of individual devices [5–14]. Moorefield et al. [15] metered 470 devices in California office buildings and found computers and monitors to account for two-thirds of the total plug load energy consumption. Hafer et al. [16] conducted a large-scale study in which they inventoried 110,0 0 0 devices in buildings on the Stanford University campus. By making energy consumption estimates for each device type, they found that laboratory equipment, computers, and monitors accounted for more than 80 percent of campus plug loads. Focusing more narrowly on the plug loads in a single building, Lanzisera et al. [5] conducted a year-long study that inventoried more than 40 0 0 devices and used 455 plug load meters to monitor the miscellaneous electric loads in an office building. From this information, they determined that representative data for their test building could have been produced if they had taken inventory of half the floor area and monitored 10 to 20 percent of their identified key devices for a two-month period. Other studies have made use of metering technology to quantify energy savings from plug load intervention strategies, such as the implementation of plug load controls and schedules or behaviorbased occupant engagement campaigns [10–12,17,18]. Many behavioral intervention strategies have been attempted to reduce plug load consumption with varying levels of success, including informational and educational campaigns, incentives and rewards programs, gamification, and energy usage feedback. Hafer et al. [12] combined energy monitoring gamification on a mobile app with plug load control schedules and achieved a 21 percent reduction in the plug loads in three buildings. In addition to behaviorbased interventions, Acker et al. [18] demonstrated energy savings in six office buildings by replacing legacy devices with ENERGY R STAR equipment and installing occupancy sensing and load sensing plug strips. While metering and intervention efforts are considered “handson” approaches, NILM techniques seek to uncover disaggregated plug load information without the extensive hardware required for smart plug-based metering studies. Due to its relatively low cost and ease of installation, NILM has been a popular technology in the residential sector dating back to the 1990s, however skepticism still exists with regard to its accuracy [19,20]. NILM has yet to be proven as a viable technology in the commercial sector, as larger spaces often have a greater quantity and diversity of devices, as well as duplicate instances of the same device, which can be challenging to differentiate [21,22]. Nevertheless, research is being conducted to bring NILM to the commercial sector [21,23–25]. Although NILM is an attractive technology due to its low-cost potential for load disaggregation, at the commercial scale, smart meters remain the most applicable technology as they often provide control functionality in addition to accurate measurements. A final area of relevant research is plug load modeling, in which many studies have attempted to model commercial building plug loads [1,8,26–28]. Menezes et al. [26] tested two techniques for plug load modeling, one based on randomly selected device profiles from measured data and another based on a bottom-up approach with inputs about a building’s equipment and usage patterns. The study found that both models were more accurate in predicting metered plug loads than the commonly used benchmarking method. Other studies have investigated developing plug load models based on data from occupancy sensors [8,28]. Critical to many plug load modeling approaches is an accurate understanding of individual device load profiles. Some studies have gathered plug load metering data for this specific purpose, however, more work is needed to aggregate a comprehensive data set of plug load

B. Doherty and K. Trenbath / Energy & Buildings 204 (2019) 109480

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Fig. 3. RSF floor plan with area of study (B Wing East) in green.

Table 1 Office and non-office space types in the B Wing East. Fig. 2. Ibis Intelisocket

TM

monitoring a lamp. Space Types in B Wing East

profiles that captures the usage diversity we see across commercial buildings today [13]. Across all of these research areas, the ability to accurately quantify plug load energy consumption is of paramount importance. In residential settings, research has found that providing building occupants with disaggregated feedback results in greater energy conservation [24]. To provide accurate disaggregation in the commercial setting, smart plug technology has proven to be the most successful, but the hardware and labor required to monitor every device in a building remains expensive. For example, Wang et al. [29] investigated the business case for implementing plug load monitoring technology in an office setting and found that the initial costs were recuperated outside of the 10-year analysis period. In a field study where they installed a smart plug metering and controls system in two retail locations, Kandt and Langner [14] similarly found a low return on investment, largely due to complications with the occupants improperly using the smart plugs. Although the hardware costs for this technology will likely continue to fall, widespread smart plug implementation is still a challenging business case to make. This paper will attempt to address this issue by proposing a method for plug load disaggregation that requires only a fraction of the building’s devices to be monitored.

3. Methods 3.1. The research support facility and metering equipment The RSF is a 332,055 square foot zero energy office building that consists of three principal wings. The B and C wings were completed in 2010 and the A wing was completed in 2011. The RSF offers a unique opportunity to study plug loads because it is submetered by end use, which provides power consumption data for the specific building functions in each wing. The A and C wings each have their own plug load submeter, and the B wing has two submeters, one for the east side and one for the west side. Each meter is a PQM II (Power Quality Meter) from GE Grid Solutions that can take a variety of measurements from the electric panel, such as current, voltage, real and reactive power, and energy. The data are aggregated into a web interface called Skyspark and can be downloaded as minute-level data for detailed analysis. The submeters provide power data on the plug loads at the building level, but to learn more about device-level consumption, 114 InteliSocketsTM (Fig. 2) from Ibis Networks were installed in August 2017 to meter plug load devices in the RSF. The InteliSocketsTM are smart plug meters that can measure a device’s minute-level power consumption. The majority of the InteliSocketsTM have two outlets and during the installation each

Workstation

Non-Workstation

Open Offices Private Offices

Break Rooms Copy Rooms Collaboration Rooms Large and Small Conference Rooms Exercise Room

Lobby Lounge Library Central Monitoring Station (Surveillance)

outlet was tagged with its location and the type of device that it was monitoring. The InteliSocketsTM take measurements of power, energy, current, and voltage every 15 s and report the averages for each minute [email communication, Bethany Sparn, 2018]. They are linked in an embedded mesh network and the data for each device are collected into an InteliGatewayTM device. The data are then passed to a cloud-based InteliNetworkTM and displayed in a userfriendly, online dashboard. In our case, given the amount of data that we needed to access, we downloaded the data directly from the Ibis Networks application programming interface. The study took place in the B Wing East of the RSF (Fig. 3), which is four stories and contains the space types found in Table 1. We selected this location because many occupants in this wing are buildings researchers and potentially more amenable to having a plug load metering device on their workstation equipment. Informational emails about the smart plugs were sent to the occupants prior to installation and all occupants were allowed to opt out of the study. The smart plugs began collecting data in August 2017 and we used data from October, November, and December of 2017 because this time period had the fewest interruptions in both the submeter and smart plug data. In the period directly after the smart plugs were installed, the occupants may have been more vigilant with their energy consumption because they knew that their devices were being monitored. We investigated a study period that began nearly two months after the smart plugs were installed, which increases the likelihood that the occupants were no longer thinking about the smart plugs during the study period and had reverted to their typical behavior. Devices were chosen such that they represented typical devices found in office buildings and spanned a diverse set of space types [email communication, Bethany Sparn, 2018]. Table 2 is a list of all the devices that were metered for this study. During the study period, there were 118 devices with continuous data, which together represented 15 different device types. In all our analysis, we removed weekends, holidays, and a handful of days in which there were significant amounts of missing data (see Appendix B). Both the smart plugs and the building submeters record minute-level data and we calculated averages over every 5minute interval because 5-minute granularity has been found to provide sufficient resolution for this type of plug load analysis [5].

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B. Doherty and K. Trenbath / Energy & Buildings 204 (2019) 109480 Table 2 Estimated number of devices in the B Wing East based on the device inventory and Eqs. (1), (2), and (3), and the number of devices metered for each device type. Device Type

Space Types Device Located In

Number of Devices Metered

Estimated Number of Devices in B Wing East

AV Controller Coffee Maker Copier Desktop Server Headset Lamp Laptop Microwave Monitor Phone Charger Projector Toaster Oven TV Video Conference Camera Water Boiler (electric tea kettle)

Non-Workstation Non-Workstation Non-Workstation Both Workstation Both Both Non-Workstation Both Workstation Non-Workstation Non-Workstation Both Non-Workstation

1 1 1 1 5 19 24 4 51 1 2 1 4 1

2.0 10.4 6.0 9.0 71.2 146.3 195.9 12.9 327.3 25.8 12.2 2.9 21.6 5.0

50% 10% 17% 11% 7% 13% 12% 31% 16% 4% 16% 34% 19% 20%

Both

2

5.7

35%

TOTAL

118

854.2

14%

3.2. Device inventory To better understand the plug loads in the RSF B Wing East, we conducted a device inventory to estimate the total number of devices in the wing. When the RSF was built, the workstations were limited to a 65 W maximum and all employees were instructed to use laptop rather than desktop computers. Although there are relatively few desktop computers in the building, there are a handful of tower workstations that are effectively being operated as servers, which we refer to as desktop servers in this paper. In speaking with the building manager, we learned that the workstation limits are not as actively communicated as they were when the building was first occupied, and they are only enforced if the building manager notices energy-intensive devices during routine safety inspections [Interview, Jake Gedvilas, 2019]. Still, most workstations were relatively uniform, typically having a laptop, two monitors, a docking station or USB hub, and an LED task lamp. The consistency across the workstations allowed us to take inventory of a subset of the workstations in the wing and confidently extrapolate that inventory to the rest of the wing. Lanzisera et al. [5] demonstrated that taking an inventory of 40 percent of their building floor space produced a less than 10 percent error in their estimates for the computers and displays. Based on this finding, we chose to monitor all the spaces we could access easily without disrupting work, therefore excluding the private office spaces. We took an inventory of the open office spaces and non-workstation spaces on all four floors (Table 1), recording the type and quantity of devices we discovered. In total, since we excluded private office spaces, the inventory accounted for roughly three-quarters of the floor space in the RSF B Wing East. In addition to this walk-through inventory, we also consulted the electric panels that feed into the B Wing East’s plug load submeter to identify any additional devices covered by the submeter that we were not aware of. Based on the walk-through inventory, for each device type we determined an estimate for the number of devices present in the B Wing East. Because many workstation devices are typically tied to individual employees (e.g., laptops) and other non-workstation devices are often communally shared (e.g., microwaves), for each device type, we estimated the number of devices found in workstation spaces and non-workstation spaces separately. For workstation space types, we calculated the average number of devices per workstation and multiplied that by the number of active employ-

Estimated Percent of Devices Metered in B Wing East

ees in the B Wing East during the study period (Eq. (1)). We obtained the number of active employees from the NREL Information Technology Services department. For each non-workstation space type, we calculated the average number of devices per room of that space type and multiplied that by the number of rooms in the wing (Eq. (2)). Finally, we took the sum of the workstation and non-workstation device estimates to get an estimate for the total number of devices in the B Wing East (Eq. (3)). Table 2 contains the estimated number of devices in the B Wing East based on these calculations. Workstation T otal  =



workstation space types



Device Count × Active Employees Workstations Inventoried

(1)

Non−Workstation T otal  = non−workstation space types





Device Count × # o f Rooms in W ing Rooms Inventoried

(2)

Estimated # o f Devices in B W ing East = W orkstation T otal + Non−W orkstation T otal

(3)

3.3. Disaggregated model development The disaggregated plug load model consisted of two components. The first component was based on smart plug data gathered on the 118 metered devices and the second was based on power estimates for devices without smart plug data, which we refer to as “supplemental devices.” To build the first component, we developed an average daily load profile for each device type by calculating the mean power at each 5-minute interval across all metered devices of that type. This load profile indicated how much power was consumed by the average device of that type for each 5-minute interval of a typical workday. We then scaled each device type’s average load profile based on the estimated number of devices in the wing to better represent all the devices of that type in the B Wing East. Finally, we aggregated the scaled profiles for each device type together.

B. Doherty and K. Trenbath / Energy & Buildings 204 (2019) 109480 Table 3 Power estimates for supplemental devices. Device Type

Conference Podium Equipment (iPad) Refrigerators Microphone Charging Equipment Treadmills and Ellipticals Central Monitoring Station TVs Automatic Door Openers Exercise Room Fans

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study to offer recommendations for future disaggregation efforts that seek to use a similar methodology in other buildings.

Single Device Power Estimate (W)

# of Devices

2.37

6

0.014

65 10

13 6

0.845 0.06

9.2

7

0.067

139.6

15

2.094

8

17

0.136

100

2

0.2

Total Power Estimate (kW)

When selecting devices to meter, we focused on typical office equipment and devices that we expected to have a significant daily load due to regular use. Although having metering data on every device type in the building would have helped in the model development, it was not practical for several reasons. For example, we could not meter hard-wired devices (e.g., Automatic Door Openers) or devices that were critical to health or safety (e.g., Central Monitoring Station TVs) and given the limited number of smart plugs available, we chose not to monitor devices with small loads that were infrequently used (e.g., pencil sharpeners). In addition, there were some devices for which we originally installed smart plug meters, but the data collected were not included due to significant discontinuities over the study period. The second component of the model, therefore, comprised power estimates for seven supplemental devices for which we did not have direct smart plug data. We included them, however, because we could estimate their standby power consumption based on short-term measurements or device manufacturer information (Table 3). Although the load profiles for many of the supplemental devices are not in fact constant loads, without timeof-use information, we developed the best estimate we could of their contribution to the model’s baseload within those constraints. The refrigerators, for example, have spikes in their load profiles when the compressor cycles, but in aggregate we estimate that the 13 refrigerators in the B Wing East contribute 0.865 kW to the baseload. The supplemental device power estimates can be seen in Table 3 and an explanation of the assumptions made for each estimate can be found in Appendix A. Once the model included both the scaled smart plug data and the power estimates for supplemental devices, we compared the model to the plug load submeter data. Additionally, we investigated correlations among the power consumption patterns of different device types and the plug load submeter data. We also examined the discrepancy between our model and the plug load submeter data, how the load distribution among devices in the wing changed throughout the day, and how the plug loads vary with the days of the week. We used the disaggregated model and the insights gained from the development of this model to identify areas for plug load energy savings. To do this, we merged quantitative analyses that support the disaggregated model with insights gained through conversations with the building manager and the AV technology professional. Plug load energy use as a fraction of whole building energy use is expected to increase over the next 20 years, so insights from operations managers shine light into how plug load use is changing in the RSF [2,30]. We coupled these insights with the disaggregated model to identify devices that are candidates for plug load energy savings. Finally, we compiled the lessons learned from this

4. Results & discussion 4.1. Comparison of the model with measured data We compared the disaggregated model to the plug load submeter data to see if, on average, the model had produced an accurate representation of the plug loads. We found that the model generally matched the shape of the submeter data; however, it did not account for the entire magnitude of the plug loads. Fig. 4(a) demonstrates that the raw smart plug data only accounts for a small portion of the total plug loads and has a relatively flat shape. In Fig. 4(b), however, the shape of the model is similar to that of the plug load submeter data. Both have mostly constant baseloads during off-work hours and demonstrate a curved shape during work hours, increasing throughout the morning and falling back down in the evening. The increase during the day is caused by a variety of devices being turned on during work hours, with some of the largest increases coming from laptops, monitors, projectors, and TV screens. They also both have midday spikes, with the peak of the model’s spike occurring 25 min before that of the plug load submeter. The disaggregation reveals that the spike around noon is due to increased use of the microwaves during lunchtime (the RSF has centralized kitchenettes in which many occupants choose to heat their own lunches). Hafer et al. [12] studied office devices and similarly found the plug load consumption to be greatest during the middle of the day; however, Gandhi and Brager 2016 [10] found decreases in midday plug load consumption of office devices. Building energy modelers often use U.S. Department of Energy reference schedules when accounting for plug loads, which also assume brief dips in midday consumption. Menezes et al. [26] observed an increase in midday plug loads for an office with a kitchen and a decrease in another office that did not have a kitchen. These differences highlight the impact that a building’s space types and usage can have on its plug loads and indicate that something as simple as occupant lunch preferences can significantly impact the shape of the building’s plug load profile. Plug load disaggregation can show how building-specific occupant behavior impacts a building’s plug load profile, and, to disaggregate accurately, one should consider the unique occupant behavior within a building when selecting which devices to meter. Another spike in the model occurs around 8:00 am and is due to the coffee makers and water boilers. This spike, however, is not present in the plug load submeter data. The disagreement between the two occurs because we only collected smart plug data for one coffee maker and one water boiler. Because these devices have brief power spikes, their load profiles are very dependent on time of use. The typical times that these devices are used can change from device to device depending on the preferences of the occupants who regularly use the devices. Although we took averages over many weeks of data to generalize the profiles of the individual coffee maker and water boiler metered on the 2nd floor, there was not enough variation in the occupants’ usage of these particular devices to capture the usage diversity across the entire B Wing East. Although these specific metered devices are used consistently around 8:00 am, it is likely that the other coffee makers and water boilers in the wing are used differently. Similarly, the microwaves have spikes in their load profiles, but we were able to better capture their usage diversity because we collected power data for four different microwaves. The agreement between the plug load submeter and the spike due to the microwaves, and the disagreement between the plug load submeter and the spike due to the coffee makers and water boilers together demonstrate the importance

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Fig. 4. (a) Raw smart plug power data from the 118 metered devices and plug load submeter data for the average workday in Oct, Nov, Dec 2017. (b) Final model that includes scaled smart plug data based on inventory scaling coefficients and supplemental device power estimates. Devices that consume little power or have small constant loads have been grouped into the “Small Consumers” category.

of metering multiple instances of devices with brief and irregular power spikes. Although the model’s shape is similar to that of the plug load submeter, the model still did not account for an average of 3.0 kW (standard deviation of 1.9 kW). The discrepancy between the model and the plug load submeter is illustrated in Fig. 5. The discrepancy is, on average, 3.8 kW between 7:00 am and 6:00 pm and 2.3 kW outside of these times. Therefore, it is likely that the model is missing both constant baseloads and loads that increase during the day. There are many possible reasons for this discrepancy. First, there are a handful of devices that we know are contributing to the plug load submeter data but that we have not included in the model because we did not have direct smart

plug measurements and could not make confident estimates of their power consumption to include them as supplemental devices. Some of these devices consume very little power (e.g., adding machines) or are almost never used according to the building manager (e.g., dishwashers). That being said, there are a few devices that are likely more significant contributors. For example, there is an ice machine in the lunch area, which could consume up to 1 kW during its ice making cycle, but it was inaccessible during this study and could not be included in the model [31]. A complete list of known devices that have not been included in the model as metered or supplemental devices can be found in Appendix C. Even though we expect many of these devices to be small, irregular loads, in aggregate they could be contributing sig-

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Fig. 5. Average difference between plug load submeter data and the model.

Fig. 6. Average daily power in the B Wing East from January 2, 2014 to February 15, 2019. Holidays and dates with faulty data have been removed. The gray line represents the raw data and the blue line represents a 90-day rolling average.

nificantly to the discrepancy between the model and the plug load submeter. Second, the plug load submeter is located on the high side of a 125 kVA transformer and therefore there are efficiency losses across the transformer included in the plug load submeter data. We accounted for these losses by modeling the transformer’s efficiency as a function of its input load. We assumed that the transformer was 98.3 percent efficient at 35 percent loading, which was the NEMA TP-1 requirement when the building was constructed [32]. It is possible, however, that the transformer is not operating at this efficiency and that the losses are higher than what we have accounted for in the model. Finally, the discrepancy could be due to error in both our device count estimates and our power estimates for the supplemental devices. For example, sometimes it was unclear whether a device was actively being used or simply being stored. For these cases, we had to decide whether to include the device in the inventory or not based on the workstation setup, which inherently introduces uncertainty into our inventory counts. 4.2. Plug load submeter insights Access to building-level submeter data allows for observations of how the plug loads in a building have changed over time. As shown in Fig. 6, the average daily plug loads in the B Wing East have slowly risen in the past few years. The building manager sug-

gested that this change could be due to increases in the wing’s occupancy. Without granular occupancy data, however, we cannot determine the extent to which occupancy has affected the plug loads. Tracking occupancy of the wing is difficult because NREL allows for alternative work schedules, work from home, and remote assignments. The number of occupants who are at the office or working elsewhere is not known. We estimate that there were 170 occupants in the B Wing East during the study time frame (October 2017–December 2017) and 190 occupants almost a year later (September 2018). Another likely cause of the increased plug loads during 2018 is that the coffee shop, which contains a handful of high-power devices, was temporarily moved from the A Wing to the B Wing East from April 2018 until January 2019. Although the plug loads increased during 2017 and 2018, they remained relatively constant during the three-month study period at the end of 2017, aside from regular dips during the holidays. When evaluating how well plug loads are managed in a building, it can be helpful to investigate the difference between the mean power consumption during occupied and unoccupied hours, as seen in Fig. 7. In the cases for which we compare occupied and unoccupied hours, the core occupied hours have been defined as 9:00 am to 5:00 pm to reflect the typical workday and the core unoccupied hours have been defined as 9:00 pm to 5:00 am, during which the building is likely only occupied by security personnel. The hours in between the core occupied and unoccupied hours represent transition periods and have been excluded because they

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Fig. 7. Mean plug load submeter power in the B Wing East for each workday during the study period. The blue (dark) bars represent the occupied hours (9:00 am to 5:00 pm) and the green (light) bars represent the unoccupied hours (9:00 pm to 5:00 am). The error bars represent ± 1 standard deviation from the mean.

can be characterized by a mix of occupancy. Fig. 7 demonstrates that the average load during unoccupied hours is relatively high compared to the average load during occupied hours, indicating that many devices are on overnight. Although some devices may be required to be on at all times, this high unoccupied load indicates there are likely many opportunities for energy savings when the building is unoccupied, both overnight and during the weekends. Although the mean unoccupied power is relatively consistent for each day in Fig. 7, Friday appears to have a lower occupied load than the rest of the weekdays. Given the similarities in the standard deviations for each day of the week, we performed an analysis of variance and a Tukey multiple comparison of means with a significance level of 0.05. We found that Friday’s mean plug load power during work hours was significantly less than that of the other four days. Building energy modelers often apply a fixed schedule for weekdays and a separate schedule for weekends when modeling plug loads. These results demonstrate that in the case of the RSF, however, it would be best to include a separate schedule for Friday that is distinct from the other days. The decreased load is likely due to the fact that many occupants leave early on Fridays or work Monday through Thursday. Although this example is specific to the RSF, many office buildings can have varied occupancy throughout the week depending on a company’s policies and culture. Considering these intricacies whenever possible will likely lead to more accurate representations of a building’s plug loads.

4.3. Device power insights To learn more about individual device behavior, we first investigated the average load profiles for each device type. Fig. 8 presents the average load profiles for most of the monitored device types broken down by each day of the week. The profiles for the projector, monitor, and laptops demonstrate lower power consumption on Fridays than the rest of the days, as is consistent with Fig. 7. The projector, video conference camera, and AV controller have variations in their overnight baseload power, which is due to variation in when the devices are left on overnight. For example, of the 11 weeks analyzed in the study, the video conference camera was left on eight of the Monday nights, five of the Tuesday nights, and four, five, and six of the rest of the weekday nights, respec-

tively. These variations are what led the video conference camera’s baseload on Monday night to be higher than the other nights. It is important to recognize that although the load profiles presented in Fig. 8 demonstrate the average profile for each device type, they are not necessarily representative of any individual device’s behavior. For example, many individual laptops will have power spikes of more than 60 W under normal operation. These power spikes are brief and occur at different times each day, and there is significant variation in laptop usage from device to device. Therefore, when we take the average of all the laptops monitored, as we have done in Fig. 8, we see that the average power never exceeds 15 W. These generalized averages are useful for this study because we are interested in observing the average breakdown of plug load consumption in the wing. To accurately model any individual device’s behavior, we recommend taking a different approach, such as that presented by Menezes et al. [26], in which they randomly sample from monitored data to model a device’s behavior. To better understand how individual device types may affect a plug load model, we investigated the relationships between these device types and the plug load submeter data. We found that many devices, such as the desktop servers in Fig. 9, did not demonstrate a clear correlation with the plug load submeter. Although the desktop servers may be more likely to be on during the workday, their consumption pattern does not closely resemble that of the plug load submeter (Fig. 8). The monitors in Fig. 9, however, demonstrated a stronger correlation with the plug load submeter power consumption, given that they tend to be on when other devices are on, and off otherwise. The Spearman rank correlation plot in Fig. 9 illustrates that the monitors and the laptops are most strongly correlated with the plug load submeter data and strongly correlated with each other as well. This finding demonstrates the importance of metering laptops and monitors to capture the shape of the building’s plug loads. It also suggests that if we were to repeat the study with a limited number of smart plug devices, we could meter the laptops and monitors together, leaving additional smart plugs available for more unique devices. A disaggregated breakdown of a building’s plug loads shows how the distribution of power among the devices changes throughout the day. Fig. 10 demonstrates that the desktop servers and Central Monitoring Station (CMS) TVs consume a significant portion of the power during off-work hours. The CMS has a high baseload be-

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Fig. 8. Average device load profiles of monitored devices for each day of the week during the study period. These profiles represent the average at each 5-minute interval across all devices of each device type. They are not scaled based on inventory.

Fig. 9. (Left) Scatterplots demonstrating the relationship between the plug load submeter and the desktop servers and monitors respectively. (Right) Graphic of Spearman rank correlation coefficients. The area of the circle is proportional to the absolute value of the correlation coefficient (larger circles indicate stronger correlation). Spearman rank correlation coefficients were used because the power data for each device are not normally distributed [27].

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Fig. 10. Average power for each device type from the inventory model as a percentage of the plug load submeter. This Fig. presents the same information as the bottom of Fig. 4 but is normalized so that 100 percent represents the plug load submeter power. The “Unknown” region represents the difference between the plug load submeter and our model. Devices that consume little power or have small constant loads have been grouped into the “Small Consumers” category.

Fig. 11. Mean power consumption for a subset of device types from the inventory model plotted as the percentage of the mean total plug load power in the B Wing East during occupied times (9:00 am to 5:00 pm) and unoccupied times (9:00 pm to 5:00 am). Purple (dark) circles indicate the mean power is based on measured data and yellow (light) circles indicate the mean power is estimated. The circle area is proportional to the device’s mean power during unoccupied times.

cause it is the main security surveillance station for all NREL properties and includes 15 TV screens that are on continuously. The AV controllers were also found to have a high baseload, consuming 1.5 kW for the two large conference rooms in the RSF. On the other hand, the laptops and monitors have some of the highest power draws during work hours, but account for a much smaller portion of the load during off-work hours. Around lunchtime, the microwaves become a large contributor to the plug loads, whereas they are mostly negligible throughout the rest of the day. 4.4. Examining opportunities for energy savings Further investigating the plug loads during work hours and offwork hours, Fig. 11 presents the mean percentage that each de-

vice contributes to the total plug loads during occupied hours and unoccupied hours. The figure demonstrates that the CMS TVs, AV controllers, desktop servers, and video conference cameras all account for a larger percentage of the unoccupied load than the occupied load because these devices are generally left on at all times. Although the CMS TVs and the desktop servers may not be candidates for implementing controls due to safety and data loss concerns, the AV controllers and video conference cameras could more realistically be put on schedules to turn off during unoccupied times or be controlled via occupancy sensors. With the advantage of disaggregation, we can see that turning off these two device types during unoccupied times would reduce the unoccupied load in the B Wing East by nearly a quarter.

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Table 4 Summary of information about a variety of device types including what was standard when the building was first occupied, what is standard today, and what opportunities exist for energy savings [Interview, Jake Gedvilas, 2019], [Interview, Emily Tritsch, 2019], [33]. Device Type

Building First Occupied (2011)

Coffee Maker

Standardized, all break rooms had the Mixed, occupants have brought in their own; No energy same model limitations Not included in building design Mixed, occupants have brought in their own; No energy limitations Standardized, energy-efficient model Same, broken models have been replaced with similar energy-efficient models Not included in building design Mixed, occupants have brought in their own; No energy limitations

Standardize again in all break rooms with energy-efficient model Standardize in all break rooms with energy-efficient model –

1, 6 W LED task light provided for each workstation 30 W laptop provided for each occupant, must make a request if desktop desired Occupants select one 24” (18 W) monitor or two 22” (15 W) monitors

Same, some workstations have more than 1 task lamp

–

Same

–

Occupants select one or two 24” (19 W) or 27” (36 W) monitors

–

AV Controller

–

On at all times

Projector

–

TV

Standardized, energy-efficient models

Some are set to turn off after period of inactivity during installation, but a power interruption could reset them back to factory settings Same

Video Conference Camera

–

Turns on whenever the remote is bumped

Reconfigure so that touch screen remains on at all times but the rest of the components can turn off during inactivity. If this is not feasible then implement when equipment is upgraded Make sure energy savings settings are in place, utilize occupancy controls, and/or implement a schedule Implement schedule or utilize occupancy controls Replace with system that only is turned on when actually in use and turns off due to inactivity

Copier

Centralized; Powers down when not in use

Same

Water Boiler Microwave Toaster Oven Lamp Laptop

Monitor

Today (2019)

Disaggregation allows one to quantify the energy savings potential for plug load reduction strategies, whether through the implementation of controls or through updates to more efficient equipment. In the RSF, for instance, if we were to implement controls on all nonessential devices in our study (see Appendix D), we estimate a 33 percent reduction in the unoccupied load. To achieve these savings, however, 670 individual devices would need to be to be controlled, which would require significant installation, hardware, and management costs. To make smart plug controls a much more reasonable investment, the disaggregation allows a more targeted approach in which we could still reduce the unoccupied load by 25 percent through selectively choosing only 13 devices to control (AV controllers, video conference cameras, and copiers). This type of analysis allows building owners to have more transparency into their plug loads and to make informed decisions about their energy reduction strategies. Anytime schedules are put in place, it is important to consider the specific needs of the building occupants to make sure that the schedule will not hinder device performance. The AV technology professional advised against implementing a schedule on the entire AV controller system out of concern that it would not function when desired. She suggested that the touch screens should be on at all times so that occupants can always interact with the AV controllers, but felt that the rest of the components could be shut off during times of inactivity [Interview, Emily Tritsch, 2019]. Our disaggregation analysis uncovered that the AV controller in a large conference room uses on average 750 W and is constantly on. Although this is a good candidate for controls, to shut off the equipment and allow the touch screen to remain on, the system would have to be overhauled and rewired. Therefore, a best practice is that AV technology managers should consider the energy impacts of equipment configurations before installation. Once AV systems are installed, it can be expensive to adjust them.

Opportunity for Energy Savings

Standardize in all break rooms with energy-efficient model

Implement schedule or utilize occupancy controls

The AV controller is one example of how building occupant productivity and convenience must be considered when identifying opportunities for plug load energy savings. Ideally, energy saving opportunities should not disrupt the occupants’ ability to conduct their typical tasks. Work practices evolve over time, as does office equipment (i.e., computers, monitors, presentation equipment). In the RSF, the AV technology professional replaces equipment every 4–8 years [Interview, Emily Tritsch, 2019]. In addition, building occupants have added plug-in equipment to kitchens to meet occupant needs for food preparation. We suggest that any new controls to plug loads be paired with educational campaigns for occupants to establish a new normal for working in the building. Table 4 summarizes how device standards have changed since the RSF was first occupied and where opportunities exist for energy savings. The opportunities listed here will not impact regular work practices. Table 4 qualitatively shows energy savings opportunities, and Fig. 11 does the same from a quantitative perspective. Together they show how the RSF plug load usage has evolved to meet occupant and operational needs. In Fig. 11 we see that laptops and monitors account for 23 percent of the occupied load, but only 4 percent of the unoccupied load as their usage is closely tied to individual occupants. The employees today are allotted more, larger monitors than they were eight years ago (Table 4). These newer R monitors are ENERGY STAR compliant and likely provide better visual displays more efficiently; however, the building manager indicated that the number of monitors he sees in the building today is certainly greater than when it was first occupied [Interview, Jake Gedvilas, 2019]. Fig. 11 also demonstrates that the coffee makers and water boilers have very small unoccupied loads. Therefore, a schedule to turn off these devices during unoccupied times would result in relatively small savings. The building manager noted that when the building was first occupied, there were standardized

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coffee makers in every kitchen and there were no water boilers [Interview, Jake Gedvilas, 2019]. Now, there exist many different types of coffee makers and water boilers in the kitchens because occupants have brought in their own (Table 4). Although a schedule for these devices may not provide significant energy savings, a standardization to energy-efficient models of these intermittent loads may be the best method for reducing their energy consumption. The disaggregated model of the RSF B Wing East’s plug loads provided important insight into the energy usage of office equipment and plug loads. The model prompted further investigation and motivated our discussions with the building manager and AV technology professional. Together, these conversations revealed important aspects of building occupant behavior, which is critical to consider when controlling plug loads. 4.5. Recommendations for other buildings For future studies seeking to utilize this method for plug load disaggregation, we recommend the following: 1 Understand all the plug load sources in the building. Talk to the building manager, walk the floors and conduct at least a partial inventory. The building manager can point out his or her hunches of large energy using devices, which are worth investigating. This may also reveal unexpected loads that are unique to the building, such as the CMS TVs in the RSF. 2 Consider conducting a one- to two-week pilot study on a large variety of devices. This will inform the design of the greater study. For example, light consumers and loads with little variability may warrant very little monitoring, allowing the focus to be on more energy-intensive devices or those with high usage variability. 3 Determine the intent of the greater study and use results from steps 1 and 2 to focus efforts. Also, determine which devices to monitor. These choices will be influenced by the number of metering devices available. If the study is focused on developing a comprehensive breakdown of the building’s plug loads, we recommend monitoring a large variety of devices. Device types that draw significant power only when they are used by occupants, such as coffee makers, water boilers, and microwaves, should have multiple devices monitored in order to capture their usage diversity. For office building studies, a handful of meters should also be devoted to laptops and monitors as they will likely account for a large part of the occupied load. 4 Take note of high energy consumers throughout the study, as these may be good candidates for controls. Implementation of controls or other energy savings opportunities should be managed such that daily work processes are not affected. Plan on significant occupant engagement and education. 5 During analysis, consider unique occupant schedules and job function requirements, such as those that will be in the building at night or those who work on weekends. These steps will help identify major plug loads in the building. A study that meters every single load will be more complete and provide better detail but will require more devices and labor for set-up, maintenance, and analysis. As can be seen in this paper, there is value in a study that uses a limited number of devices as long as the study design targets the devices with high energy use and variability.

vice inventory to develop a disaggregated model of the plug loads in one wing of an office building. We found that our model, when averaged over three months, was able to effectively replicate the shape of the building’s plug load submeter data but did not account for the total magnitude of the submeter data. There are likely several reasons why our model’s estimate was lower than the observed plug load submeter power consumption, but one of the largest contributors to the study’s limitations was the many device types for which we did not have smart plug data. If we were to apply the lessons presented in this paper and reallocate the smart plugs accordingly, we would expect the model to more closely match the plug load submeter data. The results of this study are limited to one wing in one office building and are therefore unique to the RSF itself. The study would have to be replicated in other, similarly submetered buildings to understand how effective it is at disaggregating plug loads in general. Although the work presented here is specific to the devices in the RSF B Wing East, we believe this method could be applied to the other wings of the RSF and to other buildings, as a low-cost strategy for plug load disaggregation. To best utilize this method in a timely and cost-effective manner, one would need a thorough inventory and a strong understanding of the devices in the building at the onset of the investigation. It would also be helpful to have an understanding of what devices are entering and leaving the building. This information would allow for a better allocation of smart plug devices with relative device importance and usage diversity in mind. To make this disaggregation method more robust, future work could investigate how many devices of each type must be monitored in order to accurately represent all of a building’s plug loads. For example, future studies could monitor all coffee makers in a building, apply this method to different subsets of the collected data, and observe how the accuracy of the estimates change depending on which subset is used. Although the data collected in our study were not comprehensive enough to do so, a sensitivity analysis like this could be of great value to the research community and to building managers at large. The model’s strength is its ability to provide an accurate disaggregation of a building’s average plug loads. If one were more interested in using the smart plug data to predict the building’s total plug loads at specific points in time, then we recommended considering other modeling approaches as well, such as linear regression or machine learning algorithms. Future work could use the information from the disaggregated breakdown to implement plug load controls as an energy efficiency measure. In addition, this investigation has served as a stepping stone for the use of individual device profiles, taken from real data, to better incorporate plug loads into office building energy models. As plug loads continue to account for a growing percentage of commercial building energy use, it is becoming increasingly important to reduce these loads. A better understanding of energy consumption at the device level can lead to more informed decisions about plug load management as we seek to further improve building efficiency nationwide. Declaration of Competing Interest We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.

5. Conclusion and future research

Acknowledgements

We have demonstrated a method for combining power data from a relatively small number of smart plug meters with a de-

We would especially like to thank Rois Langner and Steve Frank for their research guidance throughout this project. We would also

B. Doherty and K. Trenbath / Energy & Buildings 204 (2019) 109480

like to thank Bethany Sparn and Lieko Earle for installing the Ibis NetworksTM apparatus and Mark Madsen for his help with the InteliSocketTM data collection. Additionally, we would like to thank Katie Vrabel, Carly Burke, Cedar Blazek, Josh Butzbaugh, Maureen McIntyre, Jake Gedvilas, Emily Tritsch, Kiley Taylor, Kira Gagne, Sarah Murphy, Suzanne Belmont, Alex Swindler, Shanti Pless, Ry Horsey, and Noah Rhodes for their support with this project. This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC3608GO28308. Funding was provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Building Technologies Office and the U.S. Department of Energy Office of Science Summer Undergraduate Laboratory Internship Program. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

tations may have been due to devices being unplugged or due to interruptions in the data recording process. In those cases, we instead determined a baseload estimate from the subset of data that did exist. In other cases, when we did not have data available directly from the Ibis InteliSocketsTM , we took brief power measurements on a device over a few hours to determine a baseload estimate. When we could not take measurements directly, but specification sheets that included power consumption were available, we based our estimates on that information. The following table summarizes these assumptions. Appendix B. Dates removed from data The study period was from 10/01/2017 to 12/31/2017 but the following dates have been excluded from the data analysis for a variety of reasons presented in Table 6. Table 6 Dates removed from data and reasons for removal. Dates 10/01/2017 11/22/2017 12/02/2017 12/18/2017

Appendix A. Assumptions for all supplemental devices We utilized a few different methods for determining power assumptions for supplemental devices. Some of the devices had Ibis InteliSocketsTM metering their consumption but we could not use the data directly due to fragmentations in the data. These fragmen-

Table 5 Summary of the assumptions made for the power estimates of the supplemental devices. Device Type

Total Power Estimate (kW)

Conference Podium Equipment (iPad)

0.014

Refrigerators

0.865

Microphone Charging Equipment

0.06

Treadmills and Ellipticals

0.067

Central Monitoring Station TVs

Automatic Door Openers

Exercise Room Fans

2.094

0.136

0.2

Explanation of Assumptions

Reason for Removal – – – –

10/02/2017 11/24/2017 12/03/2017 12/31/2017

Missing smart plug data Thanksgiving holiday Missing submeter data Winter holidays and school break

Appendix C. List of known devices that were not included in the model The following devices were found to be contributing to the B Wing East plug load submeter but they were not included in the model as metered or supplemental devices for a variety of reasons, as discussed in part C of the Methods section: • • • •

Smart plug data were available for one conference podium iPad. We calculated an average from a subset of the data. We measured three different refrigerators during the same 24-hour period and took the average power of the three. We measured one microphone charging dock for one hour and recorded the average power. Standby consumption only – we measured power consumption of one elliptical for 30 min while in standby mode. The treadmills could not be measured directly but we assumed their standby power to be the same. Smart plug data were available for four collaboration room TVs; however, the CMS TVs are never turned off. We calculated the average power draw for the smart plug data while the TVs were on. Standby consumption only – specification sheet stated a standby power consumption of 8 W and this value was used. It is likely that the load is in fact much higher during occupied hours because the devices use more than 100 W while opening. Specification sheet stated 200 W power consumption at medium speed. Of the three fans in the exercise room, we observed that one is always on.

13

• • • • • • • • • • • • • • • • • • • •

Adding machines Automatic window operators Cable boxes in exercise room Desk fans Dishwashers Electric staplers Garbage disposals Hard drives HVAC control panel Ice machine Irrigation control panel Label printer Laptop docking station Mechanical room control panel Occupancy detectors Paper shredder Pencil sharpeners Plug strips Powered desks Projector screen motors Security door locks USB hubs Walkie talkie charging dock Warming oven

Appendix D. List of “Nonessential” Devices The following devices are classified as “nonessential” because they could be turned off between the hours of 9:00 pm and 5:00 am without disrupting the building function or posing a safety or data loss threat. The specific needs of each device should be carefully considered before implementing any controls to ensure

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that regularly shutting down the device would not negatively affect their lifespan or operability during regular work hours. • • • • • • • • • • • • • • • • •

AV controllers Coffee makers Conference room podium equipment Copiers Exercise room fans Headsets Lamps Microphone charging equipment Microwaves Monitors Phone chargers Projectors Toaster ovens Treadmills and ellipticals TVs (for nonsecurity purposes) Video conference cameras Water boilers

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