Plug Load Equipment Data Collection Form
Part of a series of 34 energy efficiency checklists, data collection forms, and how-to guides, this Plug Load Equipment Data Collection Form facilitates data entry.
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Managing Army Plug Load Equipment Energy Use: Office Equipment
Office equipment covers a large and diverse group of plug load devices. Policies focused on equipment purchase, power management, and usage limitations can save the Army more than 10 million kWh and $620K per year within just four building categories. This represents a top opportunity for energy- and cost-savings identified by a recent Army plug load study.
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Managing Army Plug Load Equipment Energy Use: Breakroom Appliances
Breakroom equipment is the third highest energy-consuming plug load category in Army buildings. In addition to the vending machine measures described separately, improved purchasing and control measures for breakroom appliances can save the Army more than 9 million kWh and $550K per year in the building types evaluated.
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Managing Army Plug Load Equipment Energy Use: Vending Machines
Vending machines consume the most energy of individual plug load devices common to Army buildings. As a group, they rank fourth in total electricity consumption, behind only clothes dryers, laptop computers, and telecom loads. Upgrading 80% of the estimated 22,000 refrigerated vending machines on Army installations to the latest ENERGY STAR ® performance and optimizing their efficiency settings could save 31 million kWh and $2 million per year.
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Managing Army Plug Load Equipment Energy Use: Workstation Computing Systems
Improvements in computing system security updates and purchasing policies could save the Army as much as 85 million kWh and $5 million per year when applied to the Army’s more than one million computers. This represents the largest and highest-priority plug load energy-and cost-saving measure identified by a recent study focused on Army buildings.
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Characterizing Plug Load Energy Use and Savings Potential in Army Buildings
The Assistant Secretary of the Army (Installations, Energy and Environment) tasked the Pacific Northwest National Laboratory to examine plug loads in typical Army buildings. Plug loads (also known as miscellaneous electric loads (MELs)) represent the electricity used by appliances and devices that are plugged in or hardwired and serve functions outside of a building’s core end uses. Common plug loads include computers, printers, copiers, networking devices, refrigerators, and vending machines. They also include personal electronic devices such as televisions, smart phones, tablets, and gaming systems. Examples of hardwired MELs include elevators, air compressors, and fire and security systems. The findings from this study confirm that significant energy is consumed within Army buildings by plug load devices and hardwired MEL equipment. A number of opportunities are identified for reducing unnecessary energy use that could save the Army over $5 million per year when broadly applied. Army regulations clearly spell out expectations for the purchase and operation of information technology equipment (computers, laptops, monitors, printers, and multi-function devices). However, the policies regarding the shutdown or activation of sleep and other lower power modes after 30 minutes of inactivity (15 minutes for monitors) do not appear to be consistently followed. There are many effective approaches and pathways for impacting change as it relates to improving awareness, implementing measures, and adjusting behaviors to identify and reduce plug load energy use. The Army should prioritize and consider deploying all of these to better understand and manage plug load equipment to save energy and enhance resilience across their facilities. Engaging the building occupants who use these devices daily via outreach and education should be a strong component of the strategy. The focus should be on reducing waste without sacrificing productivity or the benefits that many of these devices provide. Continued evaluation of plug loads beyond that performed here is important to gather lessons from additional building and equipment types, and to stay aware of evolving device technology and management options. This will highlight additional needs for policies, best practices, control technologies, and education of personnel to achieve real reductions in energy waste from plug load equipment. It is recommended that this study may serve as the foundation for a broader and sustained focus on plug loads and MELs, towards simultaneously enhancing the productivity, readiness, and resilience of the Army while reducing energy use and demand, and freeing up resources to better support the mission.
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Occupancy schedules learning process through a data mining framework
Building occupancy is a paramount factor in building energy simulations. Specifically, lighting, plug loads, HVAC equipment utilization, fresh air requirements and internal heat gain or loss greatly depends on the level of occupancy within a building. Developing the appropriate methodologies to describe and reproduce the intricate network responsible for human-building interactions are needed. Extrapolation of patterns from big data streams is a powerful analysis technique which will allow for a better understanding of energy usage in buildings. A three-step data mining framework is applied to discover occupancy patterns in office spaces. First, a data set of 16 offices with 10 minute interval occupancy data, over a two year period is mined through a decision tree model which predicts the occupancy presence. Then a rule induction algorithm is used to learn a pruned set of rules on the results from the decision tree model. Finally, a cluster analysis is employed in order to obtain consistent patterns of occupancy schedules. Furthermore, the identified occupancy rules and schedules are representative as four archetypal working profiles that can be used as input to current building energy modeling programs, such as EnergyPlus or IDA-ICE, to investigate impact of occupant presence on design, operation and energy use in office buildings.
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A Perspective of Energy Codes and Regulations for the Buildings of the Future
Today’s building energy codes focus on prescriptive requirements for features of buildings that are directly controlled by the design and construction teams and verifiable by municipal inspectors. Although these code requirements have had a significant impact, they fail to influence a large slice of the building energy use pie – including not only miscellaneous plug loads, cooking equipment and commercial/industrial processes, but the maintenance and optimization of the code-mandated systems as well. Currently, code compliance is verified only through the end of construction, and there are no limits or consequences for the actual energy use in an occupied building. In the future, our suite of energy regulations will likely expand to include building efficiency, energy use or carbon emission budgets over their full life cycle. Intelligent building systems, extensive renewable energy, and a transition from fossil fuel to electric heating systems will likely be required to meet ultra-low-energy targets. This paper lays out the authors’ perspectives on how buildings may evolve over the course of the 21st century and the roles that codes and regulations will play in shaping those buildings of the future.
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Simulating energy performance of buildings: a study using eQUEST and Energy Star ® portfolio manager
Commercial buildings consume significant energy in the United States and exhibit high potential for energy use reduction through retrofits. Benchmarking and energy simulation are well established tools in the industry to identify potential improvements and measure performance. Analysis to identify most sensitive retrofit parameters to energy performance can optimize investment and available energy savings. Presented study demonstrates methodology using a static model to determine sensitivity of building design and retrofit parameters with respect to energy performance. Calibrated simulation energy models (eQUEST) of two distribution centers (A, B) are presented. A fractional factorial analysis is conducted on retrofit parameters of efficiency measures targeting the highest energy consumers, and the results are benchmarked using Energy Star® Portfolio Manager. A custom Microsoft Excel® based simulation model is created to simulate occupancy levels, lighting, plug loads, and other equipment used in various spaces throughout the day. For Building A, efficient lighting was the most influential parameter for energy savings, carbon savings and benchmarking score; whereas, for Building B, HVAC efficiency was most influential for energy and demand controlled ventilation and economizers was most influential for benchmarking score. While retrofit projects can save energy and carbon emissions, variation in source-site ratios and state grid emissions, benchmarking scores may not always reflect equivalent improvement. State grid emissions factors, natural gas composition are difficult to model and hence not considered in this study. In conclusion, the synergistic analysis presented, emphasizes the importance of benchmarking and efficiency retrofits in promoting sustainable building practices to reduce energy consumption.
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Field data collection of miscellaneous electrical loads in Northern California: Initial results
This report describes efforts to measure energy use of miscellaneous electrical loads (MELs) in 880 San Francisco Bay Area homes during the summer of 2012. Ten regions were selected for metering: Antioch, Berkeley, Fremont, Livermore, Marin County (San Rafael, Novato, Fairfax, and Mill Valley), Oakland/Emeryville, Pleasanton, Richmond, San Leandro, and Union City. The project focused on three major categories of devices: entertainment (game consoles, set-top boxes, televisions and video players), home office (computers, monitors and network equipment), and kitchen plug-loads (coffee/espresso makers, microwave ovens/toaster ovens/toasters, rice/slow cookers and wine chillers). These categories were important to meter because they either dominated the estimated overall energy use of MELs, are rapidly changing, or there are very little energy consumption data published. A total of 1,176 energy meters and 143 other sensors were deployed, and 90% of these meters and sensors were retrieved. After data cleaning, we obtained 711 valid device energy use measurements, which were used to estimate, for a number of device subcategories, the average time spent in high power, low power and “off” modes, the average energy use in each mode, and the average overall energy use. Consistent with observations made in previous studies, we find on average that information technology (IT) devices (home entertainment and home office equipment) consume more energy (15.0 and 13.0 W, respectively) than non-IT devices (kitchen plug-loads; 4.9 W). Opportunities for energy savings were identified in almost every device category, based on the time spent in various modes and/or the power levels consumed in those modes. Future reports will analyze the collected data in detail by device category and compare results to those obtained from prior studies.
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Study Highlights: Characterizing Plug Load Energy Use and Savings Potential in Army Buildings
A recent study of plug load devices in Army buildings has identified opportunities to save over 83 million kWh of electricity valued at over $5 million per year. These savings may be conservative and were identified by inventorying and monitoring plug load and miscellaneous electric load (MEL) equipment within representative Army buildings. The objective was to better understand the energy consumed by these devices and identify approaches for reducing it while enhancing resilience. This summary highlights the findings, lessons learned, and recommendations identified by the study.
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Low-Power, Load-Balancing Whole Home Electrification Solution: Cooperative Research and Development (Final Report)
Testing was conducted at the Systems Performance Laboratory in NREL’s Energy Systems Integration Facility to test the performance of 120V wall-mounted heat pumps, 120V heat pump water heaters (HPWH), Electric Vehicle Supply Equipment (EVSE), and other plug-in loads, using NeoCharge Smart Splitters and whole home energy software as appropriate. Four different simulated occupancy scenarios were used to evaluate the package of technology and controls.
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Device-level plug load disaggregation in a zero energy office building and opportunities for energy savings
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 great 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 enables building owners to make more informed decisions with respect to plug load controls and energy efficiency upgrades. Furthermore, 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.
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Quick-connect scanning tunneling microscope head with nested piezoelectric coarse walkers
To meet changing research demands, new scanning tunneling microscope (STM) features must constantly evolve. We describe the design, development, and performance of a modular plug-in STM, which is compact and stable. The STM head is equipped with a quick-connect socket that is matched to a universal connector plug, enabling it to be transferred between systems. This head can be introduced into a vacuum system via a load-lock and transferred to various sites equipped with the connector plug, permitting multi-site STM operation. Its design allows for reliable operation in a variety of experimental conditions, including a broad temperature range, ultra-high vacuum, high magnetic fields, and closed-cycle pulse-tube cooling. The STM’s compact size is achieved by a novel nested piezoelectric coarse walker design, which allows for large orthogonal travel in the X, Y, and Z directions, ideal for studying both bulk and thin film samples ranging in size from mm to μm. Its stability and noise tolerance are demonstrated by achieving atomic resolution under ambient conditions on a laboratory desktop with no vibrational or acoustic isolation. The operation of the nested coarse walkers is demonstrated by successful navigation to a μm-sized 2D sample.
Instruments & Instrumentation↗
Medical Imaging Equipment Energy Efficiency
The energy use of medical imaging equipment (MIE) in healthcare facilities is estimated to be about 5% of total site energy use. MIE energy use is only expected to increase as technology advances and more patients seek services from MIE, enabling more facilities to install additional MIE. Healthcare organizations need reliable information regarding comprehensive energy use or lifetime energy costs with which to make energy-efficient MIE procurement decisions, and there are currently no MIE energy or efficiency standards in the United States (U.S.). This fact sheet provides background on MIE energy consumption, key terms, and guidance on why and how to meter MIE, helping stakeholders make informed decisions to enhance their facility's performance.
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Navigating Cybersecurity Implications of Smart Outlets
Plug and process loads (PPLs) consume approximately one third of U.S. buildings' energy budget. As other equipment becomes more efficient, and plug-in devices diversify in the market, the percentage of PPL energy consumption is forecasted to grow. To help combat this growth, new wireless metering and control technologies (also known as 'smart outlets') are helping building owners to collect plug load data, understand usage patterns, and implement controls. Adoption of these new connected technologies motivates serious cybersecurity consideration. Because these new technologies are developing at such a rapid pace, the aim of this paper is to inform thinking about cybersecurity issues and the risks that they impose. Through this lens, we explore the pros and cons of plug load controllers, provide a framework for assessing cybersecurity of connected devices such as plug load controllers, and offer an example of how commercial building owners could use this framework to minimize their cybersecurity risks when adopting smart products. In providing this framework, this paper aims to remove some barriers to implementation of these energy efficient technologies in the built environment.
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Extremely Low-Energy Design for Army Buildings: Tactical Equipment Maintenance Facility; Preprint
This paper describes the integrated energy optimization process for buildings and building clusters and demonstrates this process for new construction projects and building retrofits. An explanation is given of how mission critical building loads affect possible site and source energy use reduction in Army buildings.
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Spatial distribution of internal heat gains: A probabilistic representation and evaluation of its influence on cooling equipment sizing in large office buildings
Internal heat gains from occupants, lighting, and plug loads are significant components of the space cooling load in an office building. Internal heat gains vary with time and space. The spatial diversity is significant, even for spaces with the same function in the same building. The stochastic nature of internal heat gains makes determining the peak cooling load to size air-conditioning systems a challenge. The traditional conservative practice of considering the largest internal heat gain among spaces and applying safety factors overestimates the space cooling load, which leads to oversized air-conditioning equipment and chiller plants. In this study, a field investigation of several large office buildings in China led to the development of a new probabilistic approach that represents the spatial diversity of the design internal heat gain of each tenant as a probability distribution function. In a large office building, a central chiller plant serves all air handling units (AHUs), with each AHU serving one or more floors of the building. Therefore, the spatial diversity should be considered differently when the peak cooling loads to size the AHUs and chillers are calculated. Finally, the proposed approach considers two different levels of internal heat gains to calculate the peak cooling loads and size the AHUs and chillers in order to avoid oversizing, improve the overall operating efficiency, and thus reduce energy use.
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