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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Ten questions on urban building energy modeling

Buildings in cities consume up to 70% of all primary energy. To achieve cities’ energy and climate goals, it is necessary to reduce energy use and associated greenhouse gas emissions in buildings through energy conservation and efficiency improvements. Computational tools empowered with rich urban datasets can model performance of buildings at the urban scale to provide quantitative insights for stakeholders and inform their decision making on urban energy planning, as well as building energy retrofits at scale, to achieve efficiency, sustainability, and resilience of urban buildings. Designing and operating urban buildings as a group (from a city block to a district to an entire city) rather than as single individuals requires simulation and optimization to account for interactions among buildings and between buildings and their surrounding urban environment, and for district energy systems serving multiple buildings with diverse thermal loads across space and time. When hundreds or more buildings are involved in typical urban building energy modeling (UBEM) to estimate annual energy demand, evaluate design or retrofit options, and quantify impacts of extreme weather events or climate change, it is crucial to integrate urban datasets and UBEM tools in a seamless automatic workflow with cloud or high-performance computing for users including urban planners, designers and researchers. This paper presents ten questions that highlight significant UBEM research and applications. The proposed answers in this work aim to stimulate discussion and provide insights into the current and future research on UBEM, and more importantly, to inspire new and important questions from young researchers in the field.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Long-Term Assessment of Commercial Building Energy and Carbon Emissions in the Northwestern Region Under Future Weather Trend

The future climate significantly impacts building performance and increases uncertainties in energy simulations. A rising temperature trend is expected to heighten cooling loads during summer and result in more carbon emissions. Understanding the impact of future climate on building performance is significant for policymakers to make informed decisions. Building retrofit measures can improve building energy efficiency and reduce operational carbon emissions, yet their effects under future climate conditions have not been fully investigated so far. Thus, we proposed an assessment methodology for evaluating long-term energy consumption and operational carbon reduction potential using a building stock dataset. For this study, commercial buildings in the northwestern (NW) region were utilized to assess the impacts of future climate and building retrofit. In addition, we selected Montana with a cold and dry climate as an example to analyze and discuss the carbon emission reduction potential in buildings. The main findings are: (1) Under future climate trends, changes in energy use intensity (EUI) will fluctuate due to variations in heating and cooling degree-days (HDDs and CDDs) and increasing HDDs will lead to increasing EUI. (2) After applying annual building retrofitting, the long-term EUI reduction potential of buildings in the NW region will decrease with the increasing retrofitting degree, and the short-term EUI reduction potential will be impacted by the change of heating and cooling degree days. (3) In Montana, the long-term carbon intensity reduction potential of retrofitted buildings will decrease under future climate trends with the increasing renewable energy penetration.

building energy modeling↗
DOE OSTI2016

A regression-based approach to estimating retrofit savings using the Building Performance Database

Retrofitting building systems is known to provide cost-effective energy savings. This article addresses how the Building Performance Database is used to help identify potential savings. Currently, prioritizing retrofits and computing their expected energy savings and cost/benefits can be a complicated, costly, and an uncertain effort. Prioritizing retrofits for a portfolio of buildings can be even more difficult if the owner must determine different investment strategies for each of the buildings. Meanwhile, we are seeing greater availability of data on building energy use, characteristics, and equipment. These data provide opportunities for the development of algorithms that link building characteristics and retrofits empirically. In this paper we explore the potential of using such data for predicting the expected energy savings from equipment retrofits for a large number of buildings. We show that building data with statistical algorithms can provide savings estimates when detailed energy audits and physics-based simulations are not cost- or time-feasible. We develop a multivariate linear regression model with numerical predictors (e.g., operating hours, occupant density) and categorical indicator variables (e.g., climate zone, heating system type) to predict energy use intensity. The model quantifies the contribution of building characteristics and systems to energy use, and we use it to infer the expected savings when modifying particular equipment. We verify the model using residual analysis and cross-validation. We demonstrate the retrofit analysis by providing a probabilistic estimate of energy savings for several hypothetical building retrofits. We discuss the ways understanding the risk associated with retrofit investments can inform decision making. The contributions of this work are the development of a statistical model for estimating energy savings, its application to a large empirical building dataset, and a discussion of its use in informing building retrofit decisions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Energy savings modelling of re-tuning energy conservation measures in large office buildings

Today, many large commercial buildings use sophisticated building automation systems (BASs) to manage a wide range of building equipment. While the capabilities of BASs have increased over time, many buildings still do not fully use the BAS’s capabilities and are not properly commissioned, operated or maintained, which leads to inefficient operation, increased energy use, and reduced lifetimes of the equipment. This paper investigates the energy savings potential of several common HVAC system re-tuning measures on a typical large office building, using the Department of Energy’s building energy modeling software, EnergyPlus. The baseline prototype model uses roughly as much energy as an average large office building in existing building stock, but does not utilize any re-tuning measures. Individual re-tuning measures simulated against this baseline include automatic schedule adjustments, damper minimum flow adjustments, thermostat adjustments, as well as dynamic resets (set points that change continuously with building and/or outdoor conditions) to static pressure, supply-air temperature, condenser water temperature, chilled and hot water temperature, and chilled and hot water differential pressure set points. Six combinations of these individual measures have been formulated – each designed to conform to limitations to implementation of certain individual measures that might exist in typical buildings. All the individual measures and combinations were simulated in 16 climate locations representative of specific U.S. climate zones. The modeling results suggest that the most effective energy savings measures are those that affect the demand-side of the building (air-systems and schedules). Many of the demand-side individual measures were capable of reducing annual total HVAC system energy consumption by over 20% in most cities that were modeled. Supply side measures affecting HVAC plant conditions were only modestly successful (less than 5% annual HVAC energy savings for most cities for all measures). Combining many of the re-tuning measures revealed deep savings potential. Some of the more aggressive combinations revealed 35-75% reductions in annual HVAC energy consumption, depending on climate and building vintage.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

BuildingsBench: A Benchmark for Universal Building Load Forecasting [SWR-23-51]

The residential and commercial building stock in the United States is responsible for a significant percentage of energy consumption and greenhouse gas emissions. Electrification of end-uses, as well as decarbonizing the electrical grid through renewable energy sources such as solar and wind, constitutes the pathway to zero-emission buildings. Forecasting day-ahead building energy consumption is an integral part of this solution. Currently, specialized forecasting models are hand-made for each individual building, which is time-consuming, expensive, and leads to duplicated efforts. BuildingsBench is a Python software framework for training and comparing generalized machine learning models for universal building load forecasting. This challenge tasks a single foundational model to generalize its forecasts for a wide variety of buildings, across geographic regions, building types, weather patterns, and more. This software provide code for pre-training such models and subsequently evaluating their performance on a suite of hundreds of diverse real and synthetic buildings. BuildingsBench is a platform for: - Large-scale pretraining with the synthetic Buildings-900K dataset for short-term load forecasting (STLF). Buildings-900K is statistically representative of the entire U.S. building stock and is extracted from the NREL End-Use Load Profiles database. - Benchmarking on two tasks evaluating generalization: zero-shot STLF and transfer learning for STLF. We provide an index-based PyTorch Dataset for large-scale pretraining, easy data loading for multiple real building energy consumption datasets as PyTorch Tensors or Pandas DataFrames, simple (persistence) to advanced (transformer) baselines, metrics management, and more.

Emami, Patrick↗
DOE OSTI2018

Development of a Supermarket Prototype Building Model

The U.S. Department of Energy supports the development of commercial building energy codes and standards by participating in industry reviews, update processes, and providing technical analyses to support both published model codes and potential changes. In support of ANSI/ASHRAE/IES Standard 90.1 and the International Energy Conservation Code, 16 commercial prototype building models were developed that cover 80% of the commercial building floor area in the United States for new construction, including both commercial buildings and mid- to high-rise residential buildings, across all U.S. climate zones. However, the current set of commercial prototype building models does not include a supermarket building type, which is one of major building types defined in the Energy Information Administration’s Commercial Building Energy Consumption Survey. As part of an ongoing effort to expand the current Commercial Building Prototype Model suite, this paper presents the procedure used to develop the prototype supermarket building model based on multiple studies as well as a previously developed reference building model. The final set of prototype models includes 68 models for different vintages of ASHRAE Standard 90.1 (i.e., 2004, 2007, 2010, and 2013) and for 17 ASHRAE climate zones.

Im, Piljae↗
DOE OSTI2018

Modeling City Building Stock for Large-Scale Energy Efficiency Improvements using CityBES

Buildings in San Francisco consumed 52% of total primary energy. Improving building energy efficiency is one of the key strategies cities are adopting towards their energy and climate goals. Urban building energy models (UBEM) can support city managers to evaluate and prioritize energy conservation measures for investment and to design effective incentive and rebate programs. This paper introduces methods to develop a standardized dataset of city building stock, and it demonstrates the use of a UBEM tool, City Building Energy Saver (CityBES), for an urban-scale energy retrofit analysis of building stock in the city of San Francisco. CityBES is an open web-based data and computing platform providing city-scale building energy modeling and performance visualization and benchmarking. CityBES utilizes an international standard CityGML to represent the three-dimensional building stock in cities. As an application example, 940 office and retail buildings in six districts of northeast San Francisco were modeled and analyzed with CityBES to evaluate energy savings for five selected measures. The analysis found that replacing existing lighting with LED and adding an air economizer to HVAC systems are cost-effective measures with combined savings per building between 17% to 31%. The CityBES retrofit analysis feature does not require users to have deep knowledge of building systems or building energy models, which helps overcome barriers for city managers and their consultants to adopt UBEM.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Port Graham Community Building Biomass Heating Design Project

Native Village of Port Graham completed preconstruction activities to prepare for construction and operations of a cord wood biomass heating system to five or more community buildings in Port Graham, Alaska. Project Description Native Village of Port Graham (NVPG) completed preconstruction activities that pave the way towards reduced local energy costs through the construction and operations of a cord wood biomass heating system. NVPG plans include installation of a GARN WHS 3200 Boiler that uses cord wood as fuel source. Implementation of the 700,000 Btu per hour output biomass community building heat utility would heat 5-community buildings in Port Graham, Alaska. Heating system is estimated to displace 85% of the heating fuel oil or 5365 gallons of fuel on an annual basis with an estimated peak output of 600,000 Btu per hour. Estimated savings is $15,112.00 per year. The construction cost estimate made to install the new biomass boiler system is estimated $251,693.47 with an additional Boiler Building expansion cost estimated at $97,828.40. Total installed cost is estimated $349,521.87. The WHS 3200 Boiler would be placed inside a new structure at the old community Water Plant Building site that is controlled by NVPG. Design of the new biomass heat plant and hot water loop system was completed by Richmond Engineering, NVPG contractor for the project. A hot water heat loop system running off the boiler is designed to be placed underground on lands controlled by NVPG and stubbed to feed hot water to existing base board heating system in the following community buildings: 1. Anesia Anahonak Moonin Health and Dental Clinic 2. Native Village of Port Graham offices 3. Port Graham Public Safety Building/Fire Department 4. Port Graham Corporation Office Building which also houses the Port Graham Museum and Head Start Center 5. North Pacific Rim Housing Authority Workshop/Old Fire Hall Existing community buildings fuel oil heating systems are to be retro-fitted to accommodate hot water from the proposed wood-burning GARN Boiler, once installed, and rely on the existing fuel oil-fired hot water heating equipment for backup. The boiler would use an estimated 125 bone dry tons, equivalent to 100 cords, woody biomass feedstock obtained from local lands per year. Project would use local labor as described in the Port Graham Biomass Project, report completed by Chena Power, Inc. and Winters and Associates as part of the in-kind support to the U. S. Department of Energy (DOE) project for work on a project for State of Alaska’s Alaska Energy Authority (AEA). NVPG will likely initiate operations of the biomass boiler system even though several operational variations were studied. Obtaining the fuel source could be done by contractors, PGVC employees, or NVPG employees. Feeding the system would likely be done by NVPG employees. A majority of the buildings heated would be owned by NVPG. The PGVC office would be heated as well as the Old Fire Hall used as a workshop and storage area for North Pacific Rim Housing Authority. One methodology studied to charge for cost of utilizing the community building biomass system would use a percentage of use of hot water generated by the biomass hot water system based on past heating oil usage in relation to all buildings heated by biomass hot water. The method is better described in the Port Graham Biomass Project report. Fuel source agreements have been drafted to enter into agreements with area landowners. One Native allotment owner has asked Chugachmiut Forestry to begin a timber sale process to sell timber off her lands, specifically wind thrown timber that was determined to be of sufficient quantity to supply to the proposed biomass heating system for approximately 5-years. On NVPG’s behalf, Chugachmiut has presented to PGVC three different documents, attached, that could lead to a sale of woody biomass fuel for the project for up to 25-years, the expected life of the project. PGVC has signed a letter of intent to negotiate a sale of woody biomass material April 30, 2015. Chugachmiut Forestry has conducted two different field forest measurements of Native allotment lands and PGVC forest and timber lands. Lands deemed road accessible for biomass harvest were analyzed for this project. Forestry then conducted three different analyses and developed two reports to determine forest biomass on a tons per acre basis in addition to timber volume measurements taken for forest management purposes. Permits required were limited. For the biomass building, the Kenai Peninsula Borough did not require a permit. State of Alaska, Department of Public Safety, Division of Fire and Life Safety requires a plan review for fire and life safety requirements called an application for Fire and Life Safety Plan Review that would require a registered design professional to sign the document. State of Alaska State Forest Practices Act is required to be followed for any timber sale or harvest. This Act also requires consultation with Alaska Department of Fish and Game when operations are in close proximity or cross anadromous waters. Native allotment lands require following U. S. Bureau of Indian Affairs timber sale contracting process and approval.

09 BIOMASS FUELS↗
DOE OSTI2016

U.S.– India Joint Center for Building Energy Research and Development (CBERD) Caring for the Energy Health of Healthcare Facilities

The U.S.-India Joint Center for Building Energy Research & Development (CBERD), created through the Partnership to Accelerate Clean Energy (PACE) agreement between the United States and India, is a research and development (R&D) center with over 30 institutional and industry partners from both nations. This five-year presidential initiative is jointly funded by the U.S. Department of Energy and the Government of India. CBERD aims to build upon a foundation of collaborative knowledge, tools, and technologies, and human capabilities that will increase development of high-performance buildings. To reach this goal, the R&D focuses on energy use reduction throughout the entire life cycle of buildings—i.e., design, construction, and operations. During the operations phase of buildings, even with best-practice energy-efficient design, actual energy use can be much higher than the design intent. Every day, much of the energy consumed by buildings serves no purpose (Roth et al. 2005). Building energy information systems (EIS) are commercially available systems that building owners and facility managers use to assess their building operations, measure, visualize, analyze, and report energy cost and consumption. Energy information systems can enable significant energy savings by tracking energy use, identifying consumption patterns, and benchmarking performance against similar buildings, thereby identifying improvement opportunities. The CBERD team has identified potential energy savings of approximately 2 quads of primary energy in the United States, while industry building energy audits in India have indicated potential energy savings of up to 30 percent in commercial buildings such as offices. Additionally, the CBERD team has identified healthcare facilities (e.g., hospitals, clinics), hotels, and offices as the three of the highest-growth sectors in India that have significant energy consumption, and that would benefit the most from implementation of EIS.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Section 106 Recordation, Interpretation, and Documentation for the Demolition of Buildings 9201-5 and 9204-4, Y-12 National Security Complex, Oak Ridge, Tennessee

In 2019 and 2020, Cultural Resource Analysts, Inc. (CRA), began preparing recordation packages for 18 World War II and Cold War Era buildings slated for demolition at the request of Consolidated Nuclear Security, LLC (CNS), on behalf of the U.S. Department of Energy (DOE), National Nuclear Security Administration (NNSA). The recordations for these 18 buildings, which are located within what is now known as the Y-12 National Security Complex (Y-12) in Oak Ridge, Tennessee, were prepared and submitted in two separate packages. The first report (EC-NP-004) was prepared as a mitigation measure to address adverse effects that will result from the proposed demolition of 16 support and ancillary buildings. The current report is the second of the two packages and addresses the last two of the 18 buildings slated for demolition (9201-5 and 9204-4), which are two of the large process buildings located at Y-12. All 18 of these buildings have previously been determined eligible for listing in the National Register of Historic Places (NRHP). The two buildings, Building 9201-5 and Building 9204-4, are located south of Bear Creek Road and west of the main Y-12 entrance gate. Both buildings were constructed as uranium enrichment facilities in support of the Manhattan Project, a top-secret World War II mission to develop the world’s first nuclear weapons. Building 9201-5, completed in October 1944, is the Alpha-5 process facility and Building 9204-4, completed in Fall 1945, is the Beta-4 process facility. Over the years, these facilities were repurposed for a number of different functions.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Certified Green Building Materials: Policy Impact Assessment

Chinese building construction is rapidly expanding, making it critical to focus on energy efficiency to curb the increase of energy consumption and emissions over a building’s lifetime. A key mechanism to do so is through building material certification systems and programs. These establish how building materials will perform and can be used to promote energy and resource efficiency, which help create a more sustainable and healthy building industry. This report begins by identifying characteristics of a strong certification system and ways to improve existing systems. To curb building energy demand, the Chinese government began developing a building material certification program to encourage energy and resource efficiency in the building sector. A timeline is provided of their program’s development and provides analysis. China’s building material database is highlighted and analyzed for their significance and opportunities for improvement. The report then continues to analyze the importance of policies such as procurement programs, and the Chinese government is developing their procurement programs for certified green building materials. To understand the potential large-scale impacts of green building material certifications, we conducted an analysis of the environmental and market impact of a nationwide green building procurement policy for all new residential and commercial construction.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Prototype Supermarket Building Energy Model

The U.S. Department of Energy supports the development of commercial building energy codes and standards by participating in industry reviews, update processes, and providing technical analyses to support both published model codes and potential changes. In support of ANSI/ASHRAE/IES Standard 90.1 and the International Energy Conservation Code, 16 commercial prototype building models were developed that cover 80% of the commercial building floor area in the United States for new construction, including both commercial buildings and mid- to high-rise residential buildings, across all U.S. climate zones. However, the current set of commercial prototype building models does not include a supermarket building type, which is one of major building types defined in the Energy Information Administration’s Commercial Building Energy Consumption Survey. As part of an ongoing effort to expand the current Commercial Building Prototype Model suite, this report presents the procedure used to develop the prototype supermarket building model based on multiple studies as well as a previously developed reference building model. The final set of prototype models includes 60 models for different vintages of ASHRAE Standard 90.1 (i.e., 2004, 2007, 2010, and 2013) and for 15 ASHRAE climate zones.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

A Comprehensive Comparison of Building Height Products at a Global Level

The built environment continues to expand due to increasing human populations with some projections indicating additional growth of 2.5 billion persons by 2050. Buildings are a key component of the built environment and information on building height and urban morphology in general can help to better understand and address a host of development and urban planning issues including energy consumption, urbanization, population dynamics, urban heat islands, environmental degradation, and others. To date, several approaches have been developed to address the need for mapping building height at scale. These approaches can be grouped into two main categories: 1) directly measure height from remote sensing products or 2) indirectly estimate height from footprint morphology features at the building-by-building level. Due to a number of these global height datasets now being available for consumption and use across domains, there is a critical need to understand the differences displayed between these products and how the use of these may influence the outcome when applied. In this analysis, we compare and evaluate 5 global building height datasets: Global Human Settlement, World Settlement Footprint 3D, Global Building Morphology dataset, three-dimensional Global Building Footprints, and an approach developed at Oak Ridge National Laboratory (ORNL). Our analysis shows that, across 5 cities at a global scale with high-quality building height reference data, the approach developed by ORNL of inferring height at the building-by-building level is more accurate and consistent. This was evidenced by the ORNL product improving upon the baseline in all 5 cities, the only product to do so. These findings will aid researchers in choosing a global building height product that best meets their needs and provides global availability, consistency, and accuracy.

42 ENGINEERING↗
DOE OSTI2018

Raising the Rent Premium: Moving Green Building Research Beyond Certifications and Rent

Advocates for high-performing, energy efficient buildings understand the hurdles to proving the business case to spur development and investment decisions among commercial real estate owners and investors. Although there is significant evidence that green buildings exhibit increased financial performance via decreased operating expenses and non-energy benefits such as workplace comfort and employee productivity, green building practitioners still face an uphill battle when asked to prove the profits, most frequently pointing to rent premium as an indicator to attract investment. This paper documents a recently conducted literature review commissioned by the US Department of Energy (DOE) of 39 peer-reviewed and published academic papers pertaining to green or energy-related building attributes and financial performance. The review (first published here) indicated that 27 papers found a positive correlation between green building certifications and rents. If this is the case, then why are green building development and energy efficiency retrofits not the norm for the industry? This paper first synthesizes the recent literature review to illustrate that the existing body of empirical evidence supports green building investment. Next, it describes a workshop of leading academics in real estate sustainability and finance who met in late 2017 to discuss gaps in green building research and assess data challenges in their fields. Insights synthesized the workshop were used to drive new DOE led research that can provide value and understanding to investors and builders of high performing buildings. Finally, the paper offers insights into why the market is not properly pricing building efficiency, with factors ranging from improper mitigation of risks associated with energy and lack of transparency and responsibility felt by building end users, and what further steps are needed to transform green building investment practices in the commercial real estate industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

An insight into actual energy use and its drivers in high-performance buildings

Using portfolio analysis and individual detailed case studies, we studied the energy performance and drivers of energy use in 51 high-performance office buildings in the U.S., Europe, China, and other parts of Asia. Portfolio analyses revealed that actual site energy use intensity (EUI) of the study buildings varied by a factor of as much as 11, indicating significant variation in real energy use in HPBs worldwide. Nearly half of the buildings did not meet the American Society of Heating, Refrigerating, and Air Conditioning Engineers (ASHRAE) Standard 90.1-2004 energy target, raising questions about whether a building’s certification as high performing accurately indicates that a building is energy efficient and suggesting that improvement in the design and operation of HPBs is needed to realize their energy-saving potential. We studied the influence of climate, building size, and building technologies on building energy performance and found that although all are important, none are decisive factors in building energy use. EUIs were widely scattered in all climate zones. There was a trend toward low energy use in small buildings, but the correlation was not absolute; some small HPBs exhibited high energy use, and some large HPBs exhibited low energy use. We were unable to identify a set of efficient technologies that correlated directly to low EUIs. In two case studies, we investigated the influence of occupant behavior as well as operation and maintenance on energy performance and found that both play significant roles in realizing energy savings. We conclude that no single factor determines the actual energy performance of HPBs, and adding multiple efficient technologies does not necessarily improve building energy performance; therefore, an integrated design approach that takes account of climate, technology, occupant behavior, and operations and maintenance practices should be implemented to maximize energy savings in HPBs. As a result, these findings are intended to help architects, engineers, operators, and policy makers improve the design and operation of HPBs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Automatic and rapid calibration of urban building energy models by learning from energy performance database

Urban building energy modeling (UBEM) is attracting increasing attention in the energy modeling filed. Unlike modeling a single building using detailed building systems information, UBEM generally uses limited high-level building stock data to infer default assumptions about building characteristics and operations. Additionally, this practice inherently brings uncertainty to UBEM. This study introduced a novel method of automatic and rapid calibration of UBEM based on the annual electricity and natural gas energy use data by learning the correlations between crucial model input parameters and the building energy use from the reference building models. A case study was presented to calibrate 72 large office buildings built before 1978 in San Francisco. Seventeen model parameters were selected and Monte Carlo sampling was used to create 1000 samples that reasonably represent the parameter space. Then 1000 simulations were performed for the reference building model to create an energy performance database. The results showed that by learning from the energy performance database, it took less than four simulation runs on average to calibrate a building model. After the calibration, the distributions of each parameter were obtained to replace their single predefined default values. For example, the default lighting power density of 21.39 W/m 2 was calibrated to be 7.50 W/m 2 on average. The case study successfully demonstrated the effectiveness of the novel calibration method for UBEM in the mild climate. The method will be further tested in future for other climate zones and other building types.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Beyond Energy Efficiency: A clustering approach to embed demand flexibility into building energy benchmarking

The intermittency of carbon-free renewables and the demand changes associated with the widespread push for electrifying the transportation and building sectors provides an opportunity for buildings to go beyond energy efficiency and push towards providing demand flexibility to the electricity grid. The duality of energy efficiency and demand flexibility is necessary for success in a sustainable and reliable energy transition. Current building energy benchmarking models are limited in their ability to integrate concepts of demand flexibility and/or utilize granular smart meter data. Thus, current benchmarking methods are focused annual energy usage and fail to incorporate how the time of use of energy consumption impacts emissions in a quickly changing energy grid. Without a more comprehensive view of energy usage and associated real-time emissions, current benchmarking methods are unlikely to realize the full decarbonization potential of buildings. New emerging data streams provide an opportunity to develop a new generation of benchmarking energy models that embed dimensions of energy efficiency, grid interactivity, and demand flexibility into their analysis. In this paper, we propose a four-step method for embedding grid interactivity and demand flexibility into building benchmarking models that utilizes emerging building and time-series electricity data streams. We first engineer features to produce a mix-type dataset that encompasses many attributes of grid-interactive and efficient buildings, and then we apply K-medoids using Gower's Distance to produce peer-group clusters. We apply the method to a case study of 306 primary and secondary schools in southern California, USA. The results show that the method effectively clusters buildings by attributes of demand flexibility and energy efficiency. The clustering results reveal patterns in inefficient building operations and demand inflexibility at the building peer group level. In conclusion, the interpretation of clusters can serve as an integrated energy efficiency and demand flexibility benchmarking model and inform performance-specific policy targeting for buildings that go beyond traditional efficiency measures.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Comparative study of air-conditioning energy use of four office buildings in China and USA

Energy use in buildings has great variability. In order to design and operate low energy buildings as well as to establish building energy codes and standards and effective energy policy, it is crucial to understand and quantify key factors influencing building energy performance. Here, this study investigates air-conditioning (AC) energy use of four office buildings in four locations: Beijing, Taiwan, Hong Kong, and Berkeley. Building simulation was employed to quantify the influences of key factors, including climate, building envelope and occupant behavior. Through simulation of various combinations of the three influencing elements, it is found that climate can lead to AC cooling consumption differences by almost two times, while occupant behavior resulted in the greatest differences (of up to three times) in AC cooling consumption. The influence of occupant behavior on AC energy consumption is not homogeneous. Under similar climates, when the occupant behavior in the building differed, the optimized building envelope design also differed. In conclusion, the optimal building envelope should be determined according to the climate as well as the occupants who use the building.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗