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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

AutoBEM: A scalable framework for nationwide building energy simulation and retrofit evaluation in the United States

This paper presents AutoBEM, an integrated, automated framework for nationwide building energy modeling and retrofit evaluation in the United States. Unlike prior UBEM platforms that either rely primarily on representative stock sampling or operate at city scale, AutoBEM automates the generation of building-resolved, physics-based EnergyPlus/OpenStudio simulation models at national scale using GIS-derived geometry, prototype-based assumptions, and standardized scalable workflows. Leveraging the Model America dataset and high-performance computing, AutoBEM generates and simulates energy models for 122.9 million buildings, representing 97.8% of the U.S. building stock. These models are being made publicly and freely available as the Model America v1.0 (MAv1) dataset. AutoBEM supports detailed, building-level assessments of energy consumption, CO2 emissions, and post-processed anthropogenic heat emissions (AHE), and evaluates 151 energy conservation measures (ECMs) using localized utility pricing and building characteristics. In addition, AutoBEM incorporates both typical and future climate conditions through integration with Typical Meteorological Year (TMY) and Future TMY (fTMY) weather data derived from IPCC scenarios. In a case study of Phoenix, Arizona, AutoBEM identified several high-efficiency HVAC upgrades and selected envelope measures with short modeled payback periods (1.5 years) for certain building types and standards. Simulations under future climate scenarios (SSP5–RCP8.5) project an 11.3% increase in electricity use and a 32% reduction in natural gas demand by 2100, underscoring the need for climate-adaptive retrofit planning. By enabling reproducible, bottom-up, and location-specific analysis at scale, AutoBEM provides a step toward a national digital twin of the built environment and supports data-driven screening and planning for decarbonization, resilience, and energy equity.

Li, Hang [ORNL] (ORCID:0000000306001920)↗
DOE OSTI2021

Coloured low-emissivity films for building envelopes for year-round energy savings

We report that buildings are responsible for over 40% of total US energy use, of which about 40% is directly related to the operation of heating, ventilation and air-conditioning (HVAC) systems. Saving energy to heat and cool buildings would contribute substantially to sustainability. Here we propose a category of coloured low-emissivity films for building walls that constitute the main component of the building thermal envelope. We demonstrate high reflectance (~90%) in infrared wavelength range and selective reflectance in the visible light wavelength range for desired colours. These films can help minimize radiative heat exchange between the indoor and outdoor environments, thus saving energy for all-year cooling and heating while satisfying the required aesthetical effect. Simulations show that these films can help reduce heat gain and loss by up to 257.6 MJ per installation wall area annually. In the case of a typical midrise apartment building, the HVAC system can save up to 124.46 GJ (equal to 9.87% of the building’s HVAC energy consumption). By rough estimation, a global CO 2 emission reduction of 1.14 billion metric tons annually could be achieved. Our work provides insights for innovative energy-saving building envelope materials that can help achieve global carbon neutrality and sustainability.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

CMaize: Simplifying inter-package modularity from the build up

There is a growing desire for inter-package modularity within the chemistry software community to reuse encapsulated code units across a variety of software packages. Most comprehensive efforts at achieving inter-package modularity will quickly run afoul of a very practical problem, being able to cohesively build the modules. Writing and maintaining build systems has long been an issue for many scientific software packages that rely on compiled languages such as C/C++. The push for inter-package modularity compounds this issue by additionally requiring binary artifacts from disparate developers to interoperate at a binary level. Thankfully, the de facto build tool for C/C++, CMake, is more than capable of supporting the myriad of edge cases that complicate writing robust build systems. Unfortunately, writing and maintaining a robust CMake build system can be a laborious endeavor because CMake provides few abstractions to aid the developer. Further, the need to significantly simplify the process of writing robust CMake-based build systems, especially in inter-package builds, motivated us to write CMaize. In addition to describing the architecture and design of CMaize, the article also demonstrates how CMaize is used in production-level software.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Implementing Nationally Determined Contributions: Building Energy Policies in India’s Mitigation Strategy

The Nationally Determined Contributions (NDCs) are allowing countries to examine options for reducing emissions through a range of domestic policies. Here we use the Global Change Assessment Model to demonstrate the potential impacts of energy policies and discuss strategies to implement India’s NDC with a focus on the buildings sector. Buildings are the largest end-use sector globally, and building energy consumption is expected to increase as income grows in developing countries. We find that implementing a range of energy efficiency policies robustly can reduce total Indian building energy use by 22% and lower total Indian carbon dioxide emissions by 9% in 2050 compared to the business as usual scenario. Among various policies, energy codes for new buildings can result in the most significant savings. For all energy efficiency policies, well-coordinated, consistent implementation is critical, which requires coordination across different departments and agencies, improving capacity of stakeholders, and developing appropriate institutions to facilitate policy implementation. Renewable energy deployment can also reduce carbon emissions from buildings, and incentives in the short term are important to ensuring strong market uptake. Effective development and implementation of building energy policies could help avoid lock-in of carbon-intensive infrastructure, while improving living standards and generating health benefits.

Yu, Sha↗
DOE OSTI2023

Decoding Ethiopian Abodes: Towards Classifying Buildings by Occupancy Type Using Footprint Morphology

Building occupancy classification plays a crucial role in urban planning, disaster management, and population modeling. Traditional methods often require extensive field surveys or detailed datasets, which can be time-consuming, expensive, and may yield incomplete or erroneous data. In this paper, we present a novel approach for classifying buildings as residential or non-residential using only building footprint data. By extracting geometric shape derivatives that characterize building morphology, we developed a high-accuracy classification model employing a combination of unsupervised and supervised learning methods. We utilized open-source data from Open Street Map, aggregating it to create binary labels for buildings based on their respective human use type. Our approach demonstrates the potential for scalability without the need for additional data sources other than building footprints and labels, offering a more efficient solution for building occupancy classification.

Adams, Daniel↗
DOE OSTI2023

A Two-Step Time-Series Data Clustering Method for Building-Level Load Profile

Residential and commercial buildings have huge potential to contribute value to improve grid resilience by participating grid services. To reveal the significant value, it is critical to estimate the grid service capability from these buildings. Unlike the large-scale distributed energy resources such as wind and solar farms, those buildings need to participate grid services in aggregation, not by individual. Therefore, it is important to appropriately group buildings for aggregation. The load profiles in the same group will have similar characteristics at the same time step, so grid operators can send the grid service signal to the customer group with a higher chance to respond at that time step. In this paper, we develop a load profile clustering method to classify the building-level load profiles for grid service capability estimation. In our two-step clustering approach, we first calculate the total load consumption for each building, clustering the load profiles based on energy consumption level. Then, we further cluster the load profiles in each energy cluster based on the load shape. The parameter selection for each clustering step is discussed. The proposed method is applied on actual building-level load profiles, and the results have proved the effectiveness of this method.

advanced metering infrastructure (AMI)↗
DOE OSTI2017

A Data Driven Pre-cooling Framework for Energy Cost Optimization in Commercial Buildings

Commercial buildings consume significant amount of energy. Facility managers are increasingly grappling with the problem of reducing their buildings’ peak power, overall energy consumption and energy bills. In this paper, we first develop an optimization framework – based on a gray box model for zone thermal dynamics – to determine a pre-cooling strategy that simultaneously shifts the peak power to low energy tariff regimes, and reduces both the peak power and overall energy consumption by exploiting the flexibility in a building’s thermal comfort range. We then evaluate the efficacy of the pre-cooling optimization framework by applying it to building management system data, spanning several days, obtained from a large commercial building located in a tropical region of the world. The results from simulations show that optimal pre-cooling reduces peak power by over 50%, energy consumption by up to 30% and energy bills by up to 37%. Next, to enable ease of use of our framework, we also propose a shortest path based heuristic algorithmfor solving the optimization problemand show that it has comparable erformance with the optimal solution. Finally, we describe an application of the proposed optimization framework for developing countries to reduce the dependency on expensive fossil fuels, which are often used as a source for energy backup.We conclude by highlighting our real world deployment of the optimal pre-cooling framework via a software service on the cloud platform of a major provider. Our pre-cooling methodology, based on the gray box optimization framework, incurs no capital expense and relies on data readily available from a building management system, thus enabling facility managers to take informed decisions for improving the energy and cost footprints of their buildings

Vishwanath, Arun↗
DOE OSTI2017

Gray-Box Approach for Thermal Modelling of Buildings for Applications in District Heating and Cooling Networks

District-heating-and-cooling (DHC) systems are a proven energy solution that has been deployed for many years in a growing number of urban areas worldwide. They comprise a variety of technologies that seek to develop synergies between the production and supply of heat, cooling, domestic hot water and electricity. Although the benefits of DHC systems are significant and have been widely acclaimed, yet the full potential of modern DHC systems remains largely untapped. There are several opportunities for development of energy efficient DHC systems, which will enable the effective exploitation of alternative renewable resources, waste heat recovery, etc., in order to increase the overall efficiency and facilitate the transition towards the next generation of DHC systems. This motivated the need for modelling these complex systems. Large-scale modelling of DHC-networks is challenging, as it has several components such as buildings, pipes, valves, heating source, etc., interacting with each other. In this paper, we focus on building modelling. In particular, we present a gray-box methodology for thermal modelling of buildings. Gray-box modelling is a hybrid of data driven and physics based models where, coefficients of the equations from physics based models are learned using data. This approach allows us to capture the dynamics of the buildings more effectively as compared to pure data driven approach. Additionally, this approach results in a simpler models as compared to pure physics based models. We first develop the individual components of the building such as temperature evolution, flow controller, etc. These individual models are then integrated in to the complete gray-box model for the building. The model is validated using data collected from one of the buildings at Lule{\aa}, a city on the coast of northern Sweden.

Saurav, Kumar↗
DOE OSTI2025

BuildingQA: A Benchmark for Natural Language Question Answering over Building Knowledge Graphs

Graph-based representations of building metadata using ontologies like Brick are vital for smart building applications, but querying them remains a challenge for practitioners. Knowledge Graph Question Answering (KGQA) systems, meant to retrieve answers from natural language questions, traditionally require large-scale training data, making them ill-suited for the specialized and data-scarce building domain. The advent of Large Language Models (LLMs) offers a paradigm shift, enabling zero-shot natural language querying without building/domain-specific training. Yet, there is no standardized benchmark for building-specific KGQA which can guide and validate research in this area. To address this gap, our work makes three primary contributions. First, we introduce the BuildingQA Benchmark Dataset, constructed through a multi-stage process of collecting practitioner data, augmenting it with LLMs for linguistic diversity, and curating a final set of 188 questions across 4 buildings. Second, we characterize the benchmark's complexity and ambiguity, introducing a novel method to quantify its "lexical gap" and providing a four-stage diagnostic framework for analyzing how systems fail. Third, we benchmark zero-shot LLM-powered KGQA systems to establish baseline performance and analyze their failure modes. Our evaluation reveals that top-performing systems achieve a maximum F1 score of only 0.38. This result does not indicate a failure of these powerful systems, but rather underscores the unique challenges posed by our benchmark. It demonstrates a critical performance gap, showing that current methods successful on general KGs struggle with the specific lexical and structural nuances of the building domain. BuildingQA1 thus provides the benchmark dataset and foundational analysis needed to drive the development of novel, domain-aware methods required to unlock the use of semantic data in buildings.

Mulayim, Ozan Baris↗
DOE OSTI2024

Field Performance of Commercial Building Load Flexibility Using Model Predictive Control

Model Predictive Control (MPC) applied to buildings is starting to see some commercial adoption by companies. However, it is hard to estimate if relative energy cost savings are enough to justify the cost of MPC implementation with few reported demonstrations. In small commercial and residential buildings, a one size-fits-all solution can help reduce implementation costs, while in very large buildings or districts the potential energy cost savings magnitude can cover more tailored solutions. This estimation becomes harder for medium to large commercial buildings, where a one-size-fits-all solution cannot be adopted and potential energy cost savings might not be sufficient to cover a tailored solution. Therefore, value propositions in addition to energy efficiency alone can make MPC technology more attractive through additional energy cost savings. One such value proposition is load shifting in response to dynamic electricity prices. On this aspect, MPC is a key technology to unlock building thermal mass for energy flexibility in response to electric grid conditions. This study shows the experimental results of MPC control of an office building in Berkeley, where different dynamic electricity price profiles were used in the MPC objective function to shift the building load and to calculate hypothetical electricity costs. Results show potential 50% cost savings with respect to the existing controller with the dynamic price scenario.

Zanetti, Ettore↗
DOE OSTI2022

Cloud-Control of Legacy Building Automation System: A case study

As Internet of Things devices and cloud-based platforms become more mature, Energy Management and Information Systems (EMIS) are increasingly gaining momentum in the building industry. In large commercial buildings, Fault-Detection and Diagnostic (FDD) and energy information systems (EIS) are now established technologies with tens of providers and thousands of deployment sites across North America. The new frontier for the EMIS technology is now represented by control systems that use advanced system optimization (ASO) methods to improve the operations of the HVAC system. Given the complexity of the integration of such systems with the existing building automation systems (BAS) and the higher risk involved with direct control of the HVAC, these systems are still emerging in the market. This paper presents the results of a project in which a start-up company partnered with a research institution to develop a cloud-based software EMIS solution and deployed it in a university campus in California. The software system included advanced sensing, data acquisition, storage and advanced control and analytics applications developed on top of the native BAS. The new platform controls ten buildings on the campus and the FDD and the ASO applications deployed on this platform were able to generate energy savings of up to 35% and 25% in certain buildings for each functionality respectively. Where the platform did not save energy, it improved building service (air quality). Lessons learned include the importance of collaborating with and training the building operators and evaluating whether the legacy system can work reliably with the new technology.

Prakash, Anand Krishnan↗
DOE OSTI2020

Semantic Interoperability to Enable Smart, Grid-Interactive Efficient Buildings

Achieving a widespread transition to grid-interactive, efficient buildings (GEBs) depends critically on there being sufficient interoperability among connected building systems. While many critical elements already exist at the technical interoperability level (TCP/IP, BACnet, etc.), a lack of interoperability in the semantic level hinders streamlined integration of interdependent applications. Semantics refers to expressing information about “things” in a way that can be consistently understood by applications. Key components of formalized semantics include identifying what a “thing” is (its “type”), defining general information about that “thing” (its characteristics or properties), and defining the appropriate relationships of that “thing” to other “things” (its function or role in a larger system). Although this might seem initially trivial, the success of smart building applications is highly dependent on maintaining consistent self-descriptive notions of the “things”. Without semantic interoperability, it is technically difficult, labor-intensive, and cost-prohibitive to enable three key objectives of GEBs: optimizing performance, automatically identifying and diagnosing faults, and delivering grid services. Industry, academia, and standards bodies have invested effort in developing information models to facilitate semantic interoperability, however, they have not been widely adopted across the U.S. commercial building portfolio. This paper will present a pathway to drive semantic interoperability through a three-pronged approach to be led by the DOE Building Technologies Office in partnership with NIST and multiple national laboratories comprising: 1) industry engagement and coordination across existing efforts; 2) a semantic interoperability standard that empowers building owners to identify and require interoperable attributes when procuring equipment and applications; 3) tools to assist in implementation and a test framework to verify compliance of products with semantic interoperability specifications. This approach is designed to accelerate the timeline for adoption of semantic interoperability specifications. The intent is to reduce soft costs associated with implementing advanced controls, fault detection and diagnostics, and other smart building technologies and use cases as a necessary step in achieving an energy efficient smart grid future.

Bergmann, Harry↗
DOE OSTI2014

Enhancements to the SHARP Build System and NEK5000 Coupling

The SHARP project for the Department of Energy's Nuclear Energy Advanced Modeling and Simulation (NEAMS) program provides a multiphysics framework for coupled simulations of advanced nuclear reactor designs. It provides an overall coupling environment that utilizes custom interfaces to couple existing physics codes through a common spatial decomposition and unique solution transfer component. As of this writing, SHARP couples neutronics, thermal hydraulics, and structural mechanics using PROTEUS, Nek5000, and Diablo respectively. This report details two primary SHARP improvements regarding the Nek5000 and Diablo individual physics codes: (1) an improved Nek5000 coupling interface that lets SHARP achieve a vast increase in overall solution accuracy by manipulating the structure of the internal Nek5000 spatial mesh, and (2) the capability to seamlessly couple structural mechanics calculations into the framework through improvements to the SHARP build system. The Nek5000 coupling interface now uses a barycentric Lagrange interpolation method that takes the vertex-based power and density computed from the PROTEUS neutronics solver and maps it to the user-specified, general-order Nek5000 spectral element mesh. Before this work, SHARP handled this vertex-based solution transfer in an averaging-based manner. SHARP users can now achieve higher levels of accuracy by specifying any arbitrary Nek5000 spectral mesh order. This improvement takes the average percentage error between the PROTEUS power solution and the Nek5000 interpolated result down drastically from over 23 % to just above 2 %, and maintains the correct power profile. We have integrated Diablo into the SHARP build system to facilitate the future coupling of structural mechanics calculations into SHARP. Previously, simulations involving Diablo were done in an iterative manner, requiring a large amount manual work, and left only as a task for advanced users. This report will detail a new Diablo build system that was implemented using GNU Autotools, mirroring much of the current SHARP build system, and easing the use of structural mechanics calculations for end-users of the SHARP multiphysics framework. It lets users easily build and use Diablo as a stand-alone simulation, as well as fully couple with the other SHARP physics modules. The top-level SHARP build system was modified to allow Diablo to hook in directly. New dependency handlers were implemented to let SHARP users easily build the framework with these new simulation capabilities. The remainder of this report will describe this work in full, with a detailed discussion of the overall design philosophy of SHARP, the new solution interpolation method introduced, and the Diablo integration work. We will conclude with a discussion of possible future SHARP improvements that will serve to increase solution accuracy and framework capability.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

Commercial and Multifamily Building Tenant Energy Usage Aggregation and Privacy

In a number of cities and states, building owners are required to disclose and/or benchmark their building energy use. This requires the building owner to possess monthly whole-building energy usage information, which can be challenging for buildings in which individual tenants have their own utility meters and accounts with the utility. Some utilities and utility regulators have turned to aggregation of customer data as a way to give building owners the whole-building energy usage data while protecting customer privacy. However, no utilities or regulators appear to have conducted a concerted statistical, cybersecurity, and privacy analysis to justify the level of aggregation selected. Therefore, the Tennant Data Aggregation Task was established to help utilities address these issues and provide recommendations as well as a theoretical justification of the aggregation threshold. This study is focused on the use case of submitting data for ENERGY STAR Portfolio Manager (ESPM), but it also looks at other potential use cases for monthly energy consumption data.

data aggergation↗
DOE OSTI2014

Human Health Science Building Geothermal Heat Pump Systems

The grant objectives of the DOE grant funded project have been successfully completed. The Human Health Building (HHB) was constructed and opened for occupancy for the Fall 2012 semester of Oakland University. As with any large construction project, some issues arose which all were overcome to deliver the project on budget and on time. The facility design is a geothermal / solar-thermal hybrid building utilizing both desiccant dehumidification and variable refrigerant flow heat pumps. It is a cooling dominant building with a 400 ton cooling design day load, and 150 ton heating load on a design day. A 256 vertical borehole (320 ft depth) ground source heat pump array is located south of the building under the existing parking lot. The temperature swing and performance over 2013 through 2015 shows the ground loop is well sized, and may even have excess capacity for a future building to the north (planned lab facility). The HHB achieve a US Green Building Counsel LEED Platinum rating by collecting 52 of the total 69 available LEED points for the New Construction v.2 scoring checklist. Being Oakland's first geothermal project, we were very pleased with the building outcome and performance with the energy consumption approximately 1/2 of the campus average facility, on a square foot basis.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

TA-03-0035 Press Building – D&D

The Press Building was constructed in 1954 with 15,073 ft 2 of floor space. It was built to house a 5000 ton double action Lake Erie hydraulic press and a uranium casting area. Missions included uranium activities associated with the Nuclear Weapons and Rover Rocket programs. At the end of the Rover program, the building continued to support various uranium materials science projects until the building was placed into a cold and dark status in 2013 and then was demolished in 2017. The building interior, the press, and associated systems were radiological contaminated and disposed of as low level waste. The demolition of this building opened up valuable real estate in the TA-3 area for the future construction of an ~11,000 Sq. Ft. Biosafety Level 2 laboratory and office building. This building will support the ongoing Bioscience Division mission at the laboratory.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Institutional Transformation (IX) 2.5 Building Module Help Manual

The Institutional Transformation (IX) building module is a software tool developed at Sandia National Laboratories to evaluate energy conservation measures (ECMs) on hundreds of DOE-2 building energy models simultaneously. In IX, ECMs can be designed through parameterizing DOE-2 building models and doing further processing via visual basic for applications subroutines. IX provides the functionality to handle multiple building models for different years, which enables incrementally changing a site of hundreds of buildings over time. It also enables evaluation of the effects of changing climate, comparisons between data and modeling results, and energy use of centralized utility buildings (CUBs). IX consists of a Microsoft Excel® user interface, Microsoft Access® database, and Microsoft Excel® CUB build utility whose functionalities are described in detail in this report. In addition to descriptions of the user interfaces, descriptions of every ECM already designed in IX is included.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Low-Cost Multi-Modal Wireless Sensor Platform for Smart Buildings

Buildings consume 73% of the energy produced in the United States [1, 2]. Advanced sensors, controls, and communications have the potential to reduce the energy consumption of buildings by 20–40% [3, 4]. Currently, installation and wiring costs for sensors are quite high, making it cost-prohibitive for building managers to deploy large quantities of advanced sensors [5]. Wireless sensors have recently been deployed in buildings to provide the information necessary for optimal control of heating, ventilation, and air-conditioning and lighting systems [6, 7]. Wireless sensors have the unique advantage of being suitable for easily retrofitting existing buildings at a minimal labor cost, and the flexibility to be placed at optimal, observable locations. However, current commercially available wireless sensors are still very expensive ($150–300/node). Thus, revolutionary technological improvements in wireless sensors are required to promote inherently low-cost manufacturing for building applications to successfully exploit the energy efficiency opportunities in buildings.In this report, we present the efforts carried out at Oak Ridge National Laboratory and Molex Inc. toward exploring the design, implementation, and performance evaluation of low-cost, self-powered wireless sensors—including temperature, relative humidity, occupancy, and indoor air quality sensors—to enable advanced control applications that reduce energy consumption in buildings. Our focus is to demonstrate a novel multifunctional sensor platform that will evolve with advances in materials technology, low-cost printing techniques, nano-antennas, and co-integration of monitoring, control, and communication circuitry. We also present a low-power and bandwidth-efficient wireless communication technology that can be driven by energy harvesting in self-powered wireless sensors. It is based on code-phase-shift keying (CPSK) spread-spectrum signaling that improves both transmitter range and power consumption. The objective is to minimize the number of components within the wireless transceiver and reduce power consumption to allow sensor networks powered by energy harvesting, thus eliminating the expense and effort of periodically replacing the sensor batteries. Current state-of-the-art radio technologies have demonstrated 25–30 mA current consumption for the transmitter. Our prototype demonstrations lowered the consumption to 4 mA using spread-spectrum-based CPSK so that solar cells can be used with indoor lighting to recharge a battery indefinitely. Therefore, the system can run with no attention or maintenance for the life of the components, which is expected to be 20 years. The developed technology has the potential to generate fully printable wireless sensors with costs on the order of $1–10/node.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗