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DOE OSTI2024

From roads to roofs: How urban and rural mobility influence building energy consumption

In this article, understanding the relationship between travel behavior and building energy use at an urban scale is crucial for developing effective energy management strategies. Mobility patterns significantly impact building occupancy, which in turn affects energy consumption. However, existing methods often focus on individual buildings, whereas geographical influences on energy usage are not adequately examined. This study addresses this gap by using transportation origin-destination (OD) data to estimate building occupancy and energy. The proposed method assigns OD trips from census block groups to the building level, incorporating building, travel survey, and census data to derive building occupancy profiles. This method was applied to urban and rural areas with 4062 buildings in 70 census block groups. We found that the OD-informed occupancy profile exhibits smoother energy consumption patterns compared with that of Department of Energy reference occupancy profiles. Our analysis reveals distinct building energy consumption patterns among groups with long and short commutes, emphasizing the effect of commute times and work schedules on residential energy usage. This framework is useful for practitioners in transportation agencies and utility companies, enabling the estimation of building energy based on mobility patterns. Overall, this study shows the potential of integrating transportation and building energy data to inform cross-sector energy management strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Cost Analysis of Annual and Monthly Net Zero Energy Performance for Multifamily Buildings in the United States

As the United States continues its progress toward sustainable construction, net zero building design is becoming an increasingly important and popular topic. Two definitions of net zero energy performance, monthly and annual, provide different levels of energy autonomy and efficiency within a building. This article analyzes the viability and incremental cost for two-, three-, and four-story multifamily apartment buildings to reach both annual and monthly net zero energy performance throughout four climate zones in the United States using baseline reference buildings that represent current construction practices. Building size plays a large role in determining the capability for a building model to reach annual or monthly net zero. Two-story buildings are capable of reaching annual net zero with an increase in construction cost of about 4.4–5.6%. Three-story buildings in warmer climates can achieve annual net zero with an increase of about 5.1%, but models in colder climates cannot reach annual net zero performance. Four-story buildings cannot achieve net zero, owing to insufficient building area for solar arrays to produce electricity. Monthly net zero energy performance was significantly harder to achieve, with the only case reaching this goal being the two-story Houston based model, at an added cost of 8.2%. Generally speaking, only small buildings in warm climates will be able to achieve monthly net zero without vastly oversizing photovoltaic systems and increasing costs without adequate payback.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Innovations in Sensors and Controls for Building Energy Management: Research and Development Opportunities Report for Emerging Technologies

Sensors, actuators, and controllers, which collectively serve as the backbone of cyberphysical systems for building energy management, are one of the core technical areas of investment for achieving the U.S. Department of Energy (DOE) Building Technologies Office's (BTO's) goals for energy affordability in the national building stock - both commercial and residential. In fact, an aggregated annual energy savings of 29% is estimated in the commercial sector alone through the implementation of efficiency measures using current state-of-the-art sensors and controls to retune buildings by optimizing programmable settings based on occupant schedules and comfort requirements, as well as detecting and diagnosing equipment operation and installation problems (Fernandez et al. 2017). Monitoring and control of building conditions and operations has advanced significantly, from the invention of the modern thermostat just before the start of the 20th century to the midcentury incorporation of direct digital control into devices, the introduction of open protocols and network communications at the end of the century, and finally the invention of cloud-based computing and additional advancements that have enabled remote operation and a proliferation of connected and intelligent devices in building automation. Despite this potential, however, two main challenges hinder widespread adoption of sensors and controls in building operations that can ensure savings for high-efficiency components and equipment (e.g., heat pumps, windows, and lighting devices), as well as additional savings from more sophisticated control architectures and algorithms. energy savings of 29% is estimated in the commercial sector alone through the implementation of efficiency measures using current state-of-the-art sensors and controls to retune buildings by optimizing programmable settings based on occupant schedules and comfort requirements, as well as detecting and diagnosing equipment operation and installation problems (Fernandez et al. 2017). Monitoring and control of building conditions and operations has advanced significantly, from the invention of the modern thermostat just before the start of the 20th century to the midcentury incorporation of direct digital control into devices, the introduction of open protocols and network communications at the end of the century, and finally the invention of cloud-based computing and additional advancements that have enabled remote operation and a proliferation of connected and intelligent devices in building automation. Despite this potential, however, two main challenges hinder widespread adoption of sensors and controls in building operations that can ensure savings for high-efficiency components and equipment (e.g., heat pumps, windows, and lighting devices), as well as additional savings from more sophisticated control architectures and algorithms.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Online transfer learning strategy for enhancing the scalability and deployment of deep reinforcement learning control in smart buildings

In recent years, advanced control strategies based on Deep Reinforcement Learning (DRL) proved to be effective in optimizing the management of integrated energy systems in buildings, reducing energy costs and improving indoor comfort conditions when compared to traditional reactive controllers. However, the scalability and implementation of DRL controllers are still limited since they require a considerable amount of time before converging to a near-optimal solution. This issue is currently addressed in literature through the offline pre-training of the DRL agent. However this solution results in two main critical issues: (1) the need to develop a building surrogate model to perform the training task, and (2) the need to perform a fine-tuning process over several training episodes to obtain a near-optimal control policy. In this context, this paper introduces an Online Transfer Learning (OTL) strategy that exploits two knowledge-sharing techniques, weight-initialization and imitation learning, to transfer a DRL control policy from a source office building to various target buildings in a simulation environment coupling EnergyPlus and Python. A DRL controller based on discrete Soft Actor–Critic (SAC) is trained on the source building to manage the operation of a cooling system consisting of a chiller and a thermal storage. Several target buildings are defined to benchmark the performance of the OTL strategy with that of a Rule-Based Controller (RBC) and two DRL-based control strategies, deployed in offline and online fashion. The strategy adopted for OTL emulates the real world implementation with a simulation process by implementing the transferred DRL agent for a single episode in the target buildings. Target buildings have the same geometrical features and are served by the same energy system as the source building, but differ in terms of weather conditions, electricity price schedules, occupancy patterns, and building envelope efficiency levels. The results show that the OTL strategy can reduce the cumulated sum of temperature violations on average by 50% and 80% respectively when compared to RBC and online DRL while enhancing the energy system operation with electricity cost savings ranging between 20% and 40%. Furthermore, the OTL agent performs slightly worse than the offline DRL controller but it does not require any modeling effort and can be implemented directly on target buildings emulating a real-world implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Scenarios of Building Energy Demand for China with a Detailed Regional Representation

Building energy consumption currently accounts for 28% of China’s total energy use and is expected to continue to grow induced by floorspace expansion, income growth, and population change. Fuel sources and building services are also evolving over time as well as across regions and building types. To understand sectoral and regional difference in building energy use and how socioeconomic, physical, and technological development influence the evolution of the Chinese building sector, this study developed a building energy use model for China downscaled into four climate regions under an integrated assessment framework. Three building types (rural residential, urban residential, and commercial) were modeled specifically in each climate region. Our study finds that the Cold and Hot Summer Cold Winter regions lead in total building energy use. The impact of climate change on heating energy use is more significant than that of cooling energy use in most climate regions. Both rural and urban households will experience fuel switch from fossil fuel to cleaner fuels. Commercial buildings will experience rapid growth in electrification and energy intensity. Improved understanding of Chinese buildings with climate change highlighted in this study will help policy makers develop targeted policies and prioritize building energy efficiency measures.

building energy use↗
DOE OSTI2018

Data-Enabled Building Energy Savings (D-E BES)

Building sector energy consumption represents a significant fraction of the overall energy consumption in urban communities. While there has been increasing focus on the development of smart environments to support energy savings in urban commercial buildings, the development of smart environments for the residential sector has been less common. Instead, urban residential energy-saving efforts have focused on improving a building's physical infrastructure, introducing energy-saving appliances, and motivating residents to adopt energy conservation practices. Herein this paper illustrates that creating an affinity between a building resident's thermal preferences and a building apartment's unregulated thermal environment represents an alternative means of generating an energy-efficient environment for multi-family, residential buildings. Two years of 15-min interval summer data, obtained from smart cyber-physical systems installed in 310 apartments across two New York City buildings, is used to develop data-enabled (D-E) models of resident thermal preference and unregulated apartment temperatures. Both of these models use a linear mixed-effects approach. The alignment of optimal resident-apartment pairs is then formulated as an integer-programming problem to explore the building energy saving (BES) potential associated with minimizing the difference between unregulated apartment temperature and the residents' thermal preference based on summer cooling loads. The work shows that the energy saving potential vary with a building's physical characteristics as well as the variation in residents' thermal preferences. The work further demonstrates that both the unregulated temperature and the resident's preferred temperature in a given apartment are not only affected by outside temperature and external relative humidity, but also strongly affected by the apartment's geographic orientation. For the specific buildings considered, up to 28% cooling energy saving is possible with full alignment. The paper provides all building monitoring data as an open source data set.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Recovery Act: Advanced Load Identification and Management for Buildings

In response to the U.S. Department of Energy (DoE)’s goal of achieving market ready, net-zero energy residential and commercial buildings by 2020 and 2025, Eaton partnered with the Department of Energy’s National Renewable Energy Laboratory (NREL) and Georgia Institute of Technology to develop an intelligent load identification and management technology enabled by a novel “smart power strip” to provide critical intelligence and information to improve the capability and functionality of building load analysis and building power management systems. Buildings account for 41% of the energy consumption in the United States, significantly more than either transportation or industrial. Within the building sector, plug loads account for a significant portion of energy consumption. Plug load consumes 15-20% of building energy on average. As building managers implement aggressive energy conservation measures, the proportion of plug load energy can increase to as much as 50% of building energy leaving plug loads as the largest remaining single source of energy consumption. This project focused on addressing plug-in load control and management to further improve building energy efficiency accomplished through effective load identification. The execution of the project falls into the following three major aspects; An intelligent load modeling, identification and prediction technology was developed to automatically determine the type, energy consumption, power quality, operation status and performance status of plug-in loads, using electric waveforms at a power outlet level. This project demonstrated the effectiveness of the developed technology through a large set of plug-in loads measurements and testing; A novel “Smart Power Strip (SPS) / Receptacle” prototype was developed to act as a vehicle to demonstrate the feasibility of load identification technology as a low-cost, embedded solution; and Market environment for plug-in load control and management solutions, in particular, advanced power strips (APSs) was studied. The project evaluated the market potential for Smart Power Strips (SPSs) with load identification and the likely impact of a load identification feature on APS adoption and effectiveness. The project also identified other success factors required for widespread APS adoption and market acceptance. Even though the developed technology is applicable for both residential and commercial buildings, this project is focused on effective plug-in load control and management for commercial buildings, accomplished through effective load identification. The project has completed Smart Receptacle (SR) prototype development with integration of Load ID, Control/Management, WiFi communication, and Web Service. Twenty SR units were built, tested, and demonstrated in the Eaton lab; eight SR units were tested in the National Renewable Energy Lab (NREL) for one-month of field testing. Load ID algorithm testing for extended load sets was conducted within the Eaton facility and at local university campuses. This report is to summarize the major achievements, activities, and outcomes under the execution of the project.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Minimizing User Burden in Building Energy Analysis

Infrared (IR) imagery can provide valuable data to building energy auditors and the developers of Building Energy Models (BEM). IR by itself is not a panacea to the challenges facing energy auditors and modelers, but when combined with other techniques and Open Studio software, IR can play an important role in the minimization of the burden of creating building energy models and the diagnosing of building energy problems. While IR can be used to detect and quantify many aspects of a building’s thermal and energy performance, in Phase I we focused on a single capability: determining the R value of exterior walls. Two key attributes of using IR methods for R value determination is that IR methods measure the actual, installed performance in-situ – as opposed to relying on manufacturer or handbook values – and they can integrate performance over a wide area and thus can accurately account for thermal bridging and spatial anomalies such as “spotty” insulation installation. There is a trade between user burden and accuracy when using IR to determine a wall’s R value. The least burdensome approach is computing R values from a single IR snapshot. The most burdensome method we considered was to combine IR images with thermocouple and heat flux sensor (HFS) measurements made over the course of several days. The more effort users expend in making the measurements, the more accurate and robust the method becomes. Through a series of field tests at the ThermoAnalytics Inc. (TAI) HQ at Calumet, MI and a simulation of a 1980s era office building in Flagstaff, AZ we investigated methods to compute the R value of exterior walls using data from IR cameras, thermocouples and heat flux sensors. To compute R values the heat flux through the wall, along with temperatures on either side of the wall, must be known, modeled, or estimated – and the temperature difference through the wall should be at least 10 C. If only outdoor IR images are used in the method, the inside ambient air temperature must be estimated, and a simulation model created to predict heat fluxes and the indoor wall temperature. The accuracy of the resulting R value depends on the accuracy of the simulation and the assumptions it employs. Applying thermocouple sensors, either as iButtons or as part of a wireless sensor network, to the walls allows for the acquisition of temperatures over time. The longer the duration of the measurements, the more accurate and robust the method will be. Even with this level of measurement effort, modeling is still required to calculate the heat flux through the wall. The addition of a heat flux sensor alleviates the need for any modeling, and that results in improved accuracy. Driving the accuracy of the methods are several factors that can be quantified. How well are the wall surface temperatures known? Modeling the heat flux through walls requires knowing the details of the surrounding radiant environment and the heat transfer due to convection. How well are these boundary conditions known? Even when the heat flux is measured, error can arise when some of the measured heat is not conducted through the wall but rather goes into the thermal mass of the wall. Thermal mass effects occur when conditions and the heat flux values are fluctuating. A technique that is not robust will suffer in accuracy when these fluctuations occur. The R value determination method must be incorporated into the Open Studio process. In our proposed process, IR images and other measured sensor data are input into a module that would send R value estimates to a building library developed by team member Skidmore, Owings, & Merrill (SOM). This library of parameterized building models would use these R values along with information about the building’s age, type, and location to query the Open Studio BCL to identify the most likely constructions for walls, roof, and windows. Being based on the IMF feature of Energyplus, the SOM building library can quickly produce Energyplus models of buildings based on a limited number of user inputs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

The Demand-Side Opportunity: The Roles of Distributed Solar and Building Energy Systems in a Decarbonized Grid

Building energy systems represent a significant and largely untapped demand-side grid resource. Buildings use about 75% of electricity in the United States, including around 80% of peak demand, meaning that changes in building energy use have significant implications for grid operations. Building energy technologies-including energy-efficient devices, flexible loads, and energy storage-can be coordinated and aggregated to perform similar functions as centralized grid assets. Recent advances in information and communication (ICT) technologies have significantly improved building energy technology automation, coordination, and aggregation capabilities. With declining costs and technological advances, building energy systems can increasingly compete with conventional grid assets such as natural gas plants. Building energy technologies enable the deployment of distributed solar photovoltaics (DPV). Energy-efficient buildings allow DPV to meet greater shares of building load while flexible loads and energy storage can reshape building load profiles to optimize the on-site use of DPV. Optimized on-site use of DPV increases the value proposition for DPV adoption and can help utilities cost-effectively integrate higher levels of DPV penetration. Together, DPV and building energy technologies could help grids achieve deep decarbonization more quickly and cost-effectively.

14 SOLAR ENERGY↗
DOE OSTI2026

Opportunities to Expand Building Efficiency Programming at Community Colleges

According to the most recent U.S. Energy and Employment Report, more than 2.3 million workers in the United States are involved in activities that reduce energy usage in buildings. This workforce supports energy efficiency from the design of buildings and their systems through the manufacturing and trade of components and supplies involved in these systems to the installation, repair, and maintenance of these systems. Less than 10% of the workers in key building efficiency occupations have a bachelor’s or higher degree, compared to ~40% of the general workforce. Thus, the community college system is a key stakeholder in training and educating a large portion of the building efficiency workforce. Despite this, the literature review conducted for this report found almost no research focused on better understanding and supporting the role of community colleges as they train this workforce at scale. This report seeks to understand how and to what extent building efficiency and advanced building technology concepts are being addressed in community colleges as well as potential pathways for schools to consider to better prepare students to enter the building efficiency industry. The first section presents information from a literature review and data analysis to provide background on the building efficiency workforce, the types of building efficiency training and education available from community colleges, and the barriers and challenges that exist in the workforce. The second section offers a series of case studies that illustrate the various ways that building efficiency content can be addressed at community colleges. The final section provides an overview of the opportunities available to community colleges as well as considerations for schools that want to increase building efficiency programming.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Simulation-aided occupant-centric building design: A critical review of tools, methods, and applications

Occupants are active participants in their built environment, affecting its performance while simultaneously being affected by its design and indoor environmental conditions. With recent advances in computer modeling, simulation tools, and analysis techniques, topics such as human-building interactions and occupant behavior have gained significant interest in the literature given their premise of improving building design processes and operating strategies. In practice, the focus of occupant-centric literature has been mostly geared towards the latter (i.e., operation), leaving the implications on building design practices underexplored. This paper fills the gap by providing a critical review of existing studies applying computer-based modeling and simulation to guide occupant-centric building design. The reviewed papers are organized along four main themes, namely occupant-centric: (i) metrics of building performance, (ii) modeling and simulation approaches, (iii) design methods and applications, and (iv) supporting practices and mechanisms. Important barriers are identified for a more effective application of occupant-centric building design practices, including the limited consideration of metrics beyond energy efficiency (e.g., occupant well-being and space planning), the limited implementation and validation of the proposed methods, and the lack of integration of occupant behavior modeling in existing building performance simulation tools. Finally, future research directions are discussed, covering large-scale international data collection efforts to move from generic assumptions about occupant behavior to specific/localized knowledge, improved metrics of measuring building performance, and improved industry practices, such as building codes, to promote an occupant-in-the-loop approach to the building design process.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm

With the development of data-driven techniques, building energy prediction on a district level has attracted increasing attention in recent years for revealing energy use patterns and reduction potentials. However, data acquisition in large building groups is difficult and adjacent buildings also interact with each other. To reduce data cost and incorporate the inter-building impact with the data-driven building energy model, this study proposes a deep learning predictive approach that fuses the building network model with a long short-term memory learning model for district-scale building energy modeling. The building network was constructed based on correlations between the energy use intensity of buildings, which can significantly reduce the computational complexity of the deep learning models for energy dynamic prediction. Five typical building groups with energy use data from 2015 to 2018 on two institutional campuses were selected to perform the validation experiment with TensorFlow. Based on the prediction error assessments, the results suggest that for total building energy use intensity prediction, the proposed model can achieve a mean absolute percentage error of 6.66% and a root mean square error of 0.36 kWh/m 2 , compared to 12.05% and 0.63 kWh/m 2 of the conventional artificial neural network model and to 11.06% and 0.89 kWh/m 2 for the support vector regression model.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Healthcare Building Sustainability Assessment tool - Sustainable Effective Design criteria in the Portuguese context

Tools and methods to improve current practices and quality in the healthcare building sector are necessary to support decision-making at different building life cycle phases. Furthermore, Healthcare Building Sustainability Assessment (HBSA) Methods are based on criteria organised into different levels, such as categories and indicators. These criteria highlight aspects of significant importance when designing and operating a sustainable healthcare building. To bring more objectivity to the sustainability assessments, the standardisation bodies (CEN and ISO) proposed core indicators that should be used in the evaluation of the environmental, societal and economic performances of buildings. Nevertheless, relying on state of the art analysis, it is possible to conclude that there are aspects of major importance for the operation of healthcare buildings that are not considered in the HBSA methods. Thus, the aim of this paper is to discuss the context of sustainability assessment methods in the field of healthcare buildings and to present a proposal for the incorporation of Sustainable-Effective Design (SED) criteria in a new HBSA method. The used research method is innovative since in the development of the list of sustainability criteria it considers the opinion of main healthcare buildings' stakeholders, the existing healthcare assessment methods and the ISO and CEN standardisation works in the field of the methods to assess the sustainability of construction works. As a result, the proposed method is composed of fifty-two sustainability indicators that cover the different dimensions of the sustainability concept to support decision making during the design of a new or retrofitted healthcare building in urban areas. - Highlights: •A new system to assess the sustainability of healthcare buildings is presented. •We propose a method to develop the list of sustainability indicators for hospitals. •We propose a new concept – Sustainable-Effective Design (SED). •The system is adapted to the Portuguese building sector sustainability contexts. •The approach can be used to develop similar systems in other countries.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Potential benefits and optimization of cool-coated office buildings: A case study in Chongqing, China

Increasing envelope facet albedos considerably reduces solar heat gain, thus yielding building cooling energy savings. Few studies have explored the potential benefits of utilizing cool coatings on building envelopes (“cool-coated buildings”) based on life-cycle cost analysis. A holistic approach integrating the field testing, building energy simulation, and a 20-year life-cycle-based optimization was developed to explore cool-coated building performance and the maximum net savings of optimal building envelope retrofit and design. Experimental results showed that applying cool coatings to a west wall of an office building in Chongqing, China reduced its exterior surface temperature by up to 9.3 °C in summer. Additionally, simulation results showed that in Chongqing, making the roof and walls cool could reduce annual HVAC electricity use by up to 11.9% in old buildings (with poorly insulated envelopes) and up to 5.9% in new buildings. Retrofitting old buildings with a cool roof provided the net savings per modified area with present values up to 42.8 CNY/m2; retrofitting a new building with a cool roof or cool walls was not cost-effective. Optimizing both envelope insulation and envelope albedo can achieve 5.6 times the net savings of optimizing the insulation only, and 1.6 times that of optimizing albedo only.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Modernising ATLAS Software Build Infrastructure

In the last year ATLAS has radically updated its software development infrastructure hugely reducing the complexity of building releases and greatly improving build speed, flexibility and code testing. The first step in this transition was the adoption of CMake as the software build system over the older CMT. This required the development of an automated translation from the old system to the new, followed by extensive testing and improvements. This resulted in a far more standard build process that was married to the method of building ATLAS software as a series of 12 separate projects from Subversion. We then proceeded with a migration of the code base from Subversion to Git. As the Subversion repository had been structured to manage each package more or less independently there was no simple mapping that could be used to manage the migration into Git. Instead a specialist set of scripts that captured the software changes across official software releases was developed. With some clean up of the repository and the policy of only migrating packages in production releases, we managed to reduce the repository size from 62 GiB to 220 MiB. After moving to Git we took the opportunity to introduce continuous integration so that now each code change from developers is built and tested before being approved. With both CMake and Git in place we also dramatically simplified the build management of ATLAS software. Many heavyweight homegrown tools were dropped and the build procedure was reduced to a single bootstrap of some external packages, followed by a full build of the rest of the stack. This has reduced the time for a build by a factor of 2. It is now easy to build ATLAS software, freeing developers to test compile intrusive changes or new platform ports with ease. We have also developed a system to build lightweight ATLAS releases, for simulation, analysis or physics derivations which can be built from the same branch.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Air Handling Unit Shutdowns During Scheduled Unoccupied Hours: US Commercial Building Stock Prevalence and Energy Impact

Commercial buildings account for 18% of U.S. energy consumption, with 44% used for heating, ventilation, and air conditioning (HVAC). American Society of Heating, Refrigerating and Air Conditioning Engineers (ASHRAE) 90.1 requires HVAC systems to shutdown fans and outdoor air ventilation during unoccupied times, only allowing fans to cycle on, without outdoor air, to maintain thermostat setpoints. However, it is minimally understood how often existing building operations align with energy code requirements and the energy implications of not doing so. This study used building automation system data from 843 buildings containing 5706 air handling units (AHUs) to determine three unoccupied AHU shutdown control schemes ranging in efficiency and then estimated their prevalence in the U.S. commercial building stock, segmented by building type. ComStock was then used to analyze the energy savings potential of implementing the most energy efficient unoccupied shutdown control scheme in non-participating buildings across the U.S commercial building stock. Results show that only 23% of AHUs align completely with the ASHRAE 90.1 requirement. ComStock modeling results show 4% annual stock energy savings by switching all non-participating buildings to the most efficient scheme, with 19% annual energy savings demonstrated for the median building switching from the least efficient scheme to the most efficient. Findings also show 114.5 TBtu electricity and 75.8 TBtu natural gas fuel savings when converting to the most efficient scheme. Furthermore, these findings help stakeholders understand the high prevalence of buildings not aligning with the ASHRAE-90.1 requirements for unoccupied AHU shutdowns and the energy savings potential of utilizing the most efficient unoccupied AHU shutdown scheme.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Potential Demand Reduction from Buildings in a Simulated Utility

The availability of large-area sensing, scalable algorithms, and high-performance computing has enabled the possibility of urban-scale building energy modeling using new methods not limited to the scalability of manual building energy model creation or retrieval of county-by-county tax assessor’s data. Automatic Building detection and Energy Model creation (AutoBEM) has created 178,368 building energy models for the Electric Power Board (EPB) of Chattanooga, TN, and compared simulation performance to 15-minute data from each building. These models leverage several data sources (e.g. imagery, GIS, utility), software tools to extract building properties (e.g. footprint, height, façade type, window-to-wall ratio, occupancy, building type), simulate at scale on two of the world’s #1 fastest supercomputers, and provide web-based visual analytics.Grid-interactive efficient buildings offer the potential to reduce utility and rate-payer energy costs during each calendar month’s hour of critical generation – when the least efficient, most costly, and often dirtiest generation assets must be brought online. In this paper, EnergyPlus is used to simulate over 150,000 buildings to assess the technical potential of utility-controlled smart thermostats. This is analyzed under a couple scenarios leveraging buildings as thermal batteries via pre-conditioning to coast through peak hours. Results are provided in box and whisker plots assessing the distribution of peak demand reduction at the utility-scale along with breakouts of energy and demand savings by building type and vintage.

Bass, Brett↗
DOE OSTI2015

Project Management Plan/Progress Report: UT/GTKS Training Program Development for Commercial Building Operators (Final Report)

Universidad del Turabo (UT), in a collaborative effort with Global Turn Key Services, Inc. (GTKS), proposed to develop a training program and a commercialization plan for the development of Commercial Building Operators (CBOs). The CBOs will operate energy efficient buildings to help maintain existing buildings up to their optimal energy performance level, and ensure that net-zero-energy buildings continuously operate at design specifications, thus helping achieve progress towards meeting BTP Strategic Goals of creating technologies and design approaches that enable net-zero-energy buildings at low incremental costs by 2025. The proposed objectives were then: (1) Develop a Commercial Building Operator (CBO) training program and accreditation that will in turn provide a certification to participants recognized by Accreditation Boards such as the North American Board of Certified Energy Practitioners (NABCEP) and Leadership in Energy & Environmental Designs (LEED). (2) Develop and implement a commercialization and sustainability plan that details marketing, deployment, financial characterization, job placement, and other goals required for long-term sustainability of the project after the funding period. (3) After program development and deployment, provide potential candidates with the knowledge and skill sets to obtain employment in the commercial building green energy (net-zero-energy building) job market. The developed CBO training program will focus on providing skills for participants, such as displaced and unemployed workers, to enter the commercial building green energy (net-zeroenergy building) job market. This course was designed to allow a participant with minimal to no experience in commercial building green technology to obtain the required skill sets to enter the job market in as little as 12 weeks of intensive multi-faceted learning. After completion of the course, the CBO staff concluded the participant will meet minimum established accreditation standards established by UT and will complete the contact hours required of training to apply to the Certification on Energy Management (CEM) offered by the Association of Energy Engineers (AEE). The CBO training program consists of a combination of theory (classroom), online & computer simulation, laboratory & hands on (onsite) training lessons. The training is addressed four basic learning elements: (1) Learn the Technology; (2) Practice Skills with hands-on the Energy Simulation Builder program; (3) Final Project and Presentation; and, (4) Accreditation and Certifications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗