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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Simulation-Based Validation of An Open-Source, Scalable Framework for Building Energy Management in Small and Medium-Sized Commercial Buildings

Abstract: Small and medium-sized commercial buildings (SMCBs) represent 94% of U.S. commercial buildings but encounter substantial obstacles in adopting Building Energy Management (BEM) systems. Current approaches exhibit fundamental limitations: vendor-specific API platforms restrict interoperability through proprietary ecosystems; commercial automation software demands extensive technical expertise and licensing costs; open-source IoT solutions lack native support for building automation protocols and semantic models. This paper introduces a configuration-driven web interface framework addressing the gap between smart device advancements and accessible BEM software infrastructure for SMCBs. The framework leverages VOLTTRON middleware integrated with an automated converter that processes unified YAML configurations into heterogeneous system files, reducing required configuration artifacts from six separate files to a single unified specification. The system architecture enables vendor-agnostic operation through BACnet and Modbus protocols while supporting semantic building model integration via automated Brick Schema parsing. Configuration-driven interfaces automatically adapt to diverse HVAC types without custom development. Simulation-based validation using BOPTEST demonstrates automatic interface generation between fan coil and hydronic systems, with the automated converter successfully generating all platform-specific outputs from the single YAML input. The result demonstrates the framework's capability to streamline BEM system deployment through reduced configuration complexity. This work bridges simulation capabilities with operational deployment, demonstrating how virtual testbeds validate generalizable software frameworks for real-world building automation.

Chung, Jihoon [ORNL] (ORCID:0000000184880815)↗
DOE OSTI2020

Building the Efficiency Workforce: Preprint

Demand for high-performance homes and buildings is growing due to their marketability, environmental benefits, and increasing affordability. Growth for energy efficient technologies and building upgrades has driven expansion across many traditional industries including construction trades (which added almost 21,000 jobs) and professional services (which added 35,000 employees) in 2018, according to the 2019 U.S. Energy Employment Report (USEER). As buildings become more automated, digitized and interconnected, the workforce that supports the design, build, operations and maintenance of these buildings must evolve. The U.S. Department of Energy’s Building Technologies Office (BTO) has engaged NREL to provide a framework upon which BTO can develop their next generation workforce development strategy. The strategy will be designed to increase quantity of workers with the skills to adequately design, install, maintain and operate high- performance buildings across all building sectors (residential, commercial, industrial). This paper will summarize past BTO workforce development efforts, identify current skills gaps and hiring challenges, and present anticipated future workforce needs based on new technologies currently being developed by labs and industry. Through a literature review and a series of industry and academic workshops, NREL will present a framework that includes multiple options to best prepare the next generation workforce.

buildings↗
DOE OSTI2026

Field Validation of a Grid-Interactive Efficient Building Software Solution

The U.S. General Services Administration's (GSA's) Green Proving Ground (GPG) program, in partnership with the National Laboratory of the Rockies (NLR), completed a field study of a Grid-Interactive Efficient Buildings (GEB) software solution. The study focused on a single testbed facility to test the GEB functionality of the software solution, along with other features. The testbed facility - a courthouse - is a common building type in GSA's vast building portfolio, offering potentially impactful findings on a scalable level. The study evaluated Prescriptive Data's technology, Nantum OS, a connected building operating system ("GEB Solution") which aggregates multiple sources of previously siloed building data and combines that data with external sources, such as weather information or utility signals, into a single integrated platform. A GEB Solution is a type of Energy Management Information System (EMIS). EMIS is defined as a system of devices, data services, and software applications that communicates with any building system or third-party data source to aggregate and transform data into new capabilities to aid in the optimization of energy use at the building, campus, or agency level. This specific GEB Solution is an EMIS with ASO, automated system optimization, offering supervisory control of certain aspects of the Building Automation System (BAS). Multiple features were evaluated including, but not limited to, Continuous Demand Management to avoid setting new monthly kilowatt (kW) peaks, energy efficiency for reduction of kilowatt hours (kWh) and natural gas consumption, and automated demand response (ADR) for purposes of lowering demand during a utility called Demand Response (DR) event. The testbed facility was the Foley Federal Building and US Courthouse ("Foley Federal Building") located in Las Vegas, NV. This is a 209,496 sq. ft. building constructed in the 1960s with major renovations in 2004. The facility was a good candidate due to the large prevalence of office and courthouse spaces in the GSA portfolio of buildings. It also has many features which allow integration into and control of the building and a strong facilities team to assist with the study. Quantitative and qualitative performance objectives were developed using GSA's GPG GEB project template along with input from the vendor and building facility staff; these are outlined in Table 1. The quantitative performance objectives focused on continuous demand management, energy efficiency, and automated demand response. The qualitative performance objectives focused on the ease of installation and commissioning as well as the operability of the GEB solution. Other performance metrics that are reported on include carbon reduction, cost effectiveness, and occupant acceptance.

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

Transformative Efficiency and Automation in Modular Homes (TEAMH)

This report documents the Transformative Efficiency and Automation in Modular Homes (TEAMH) project, which evaluates the integration of advanced building envelope technologies and automation-assisted modular construction to improve residential energy performance and construction efficiency. The study investigates high-performance insulation systems, including vacuum insulation panels (VIPs), combined with light gauge steel (LGS) modular construction and factory automation. Laboratory testing, whole-building energy modeling across multiple climate zones, and factory demonstrations were conducted to assess thermal performance, energy savings, and production efficiency. Results indicate that upgraded envelope assemblies can achieve up to ~50% heating and ~34% cooling energy savings relative to IECC 2018 code-compliant homes, while automation-assisted construction can reduce wall assembly time by 24%–46% compared to conventional wood framing. The findings demonstrate the potential for scalable, high-performance modular homes that deliver significant energy savings with competitive projected costs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Alfalfa Virtual Building Service: Software Engineering Best Practices Applied to Runtime Interaction with Building Energy Models

Buildings are active participants in increasingly complex energy systems. Building Energy Modeling (BEM) has a key role to play in planning and de-risking an equitable energy transition, with BEM-backed "virtual buildings" critical path for diverse applications that include workforce training tools, Hardware-in-the-Loop (HIL) experimentation to study equipment performance under a range of conditions, Control-Hardware-in-the-Loop (CHIL) experimentation to de-risk commercial control implementations at equipment through grid orchestration levels, and integration of dynamic load profiles into grid modeling tools for energy system experimentation at the urban scale. Modeling requirements vary across these applications, but many software engineering tasks do not. The Alfalfa Virtual Building Service (AVBS, see https://github.com/NREL/alfalfa/wiki) is an open-source web service that solves these common tasks robustly in one place, providing a foundational platform for power users to bootstrap their own applications. AVBS abstracts the specifics of runtime interaction with OpenStudio, Modelica, and Spawn of EnergyPlus models behind a unified REST API. Additionally, AVBS provides resources for cloud deployment and scaling to 100s of parallel simulations, a growing library of modular Operational Technology (OT) integrations for emulation of real-world interfaces, and scripts to automate the population of communities of virtual buildings from URBANopt, ResStock and ComStock.

building automation↗
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 OSTI2025

Automated Strain Construction for Biosynthetic Pathway Screening in Yeast

Automation accelerates the Design-Build-Test-Learn (DBTL) cycle for synthetic biology; however, most strain construction pipelines lack robotic integration. Here, in this study, we present the workflow design and source code for a modular, integrated protocol that automates the Build step in Saccharomyces cerevisiae. We programmed the Hamilton Microlab VANTAGE to integrate off-deck hardware via its central robotic arm, enabling automated steps that increased throughput to 2,000 transformations per week. We developed a user interface with the Hamilton VENUS software to support on-demand parameter customization. As a proof of concept, we screened a gene library in an engineered yeast strain producing verazine, a key intermediate in the biosynthesis of steroidal alkaloids. Our pipeline rapidly identified pathway bottlenecks and genes that enhanced verazine production by 2.0- to 5-fold. This technical note provides resources for synthetic biologists designing yeast workflows for biofoundries to screen libraries for pathway discovery/optimization, combinatorial biosynthesis, and protein engineering.

automation↗
DOE OSTI2024

Advancing Building Energy Modeling with Large Language Models: Exploration and Case Studies

The rapid progression in artificial intelligence has facilitated the emergence of large language models like ChatGPT, offering potential applications extending into specialized engineering modeling, especially physics-based building energy modeling. This paper investigates the innovative integration of large language models with building energy modeling software, focusing specifically on the fusion of ChatGPT with EnergyPlus. A literature review is first conducted to reveal a growing trend of incorporating large language models in engineering modeling, albeit limited research on their application in building energy modeling. We underscore the potential of large language models in addressing building energy modeling challenges and outline potential applications including simulation input generation, simulation output analysis and visualization, conducting error analysis, co-simulation, simulation knowledge extraction and training, and simulation optimization. Three case studies reveal the transformative potential of large language models in automating and optimizing building energy modeling tasks, underscoring the pivotal role of artificial intelligence in advancing sustainable building practices and energy efficiency. The case studies demonstrate that selecting the right large language model techniques is essential to enhance performance and reduce engineering efforts. The findings advocate a multidisciplinary approach in future artificial intelligence research, with implications extending beyond building energy modeling to other specialized engineering modeling.

building energy modeling↗
DOE OSTI2020

Rapid Annotation of Photosynthetic Systems (RAPS): automated algorithm to generate genome-scale metabolic networks from algal genomes

Algae have mounting potential to serve as platform strains for engineering efforts, but tools for these species lag behind traditional model systems like E. coli and yeast. Metabolic models are one such tool that would enhance our ability to rationally engineer algae but current automated algorithms to build models do not perform well for algal genomes. We introduce a novel software pipeline, Rapid Annotation of Photosynthetic Systems (RAPS), which leverages manual curation efforts of published models to create high quality first draft metabolic networks for new species. We compared models produced by our pipeline to published models and found that these models are able to capture more genes than the published models and are more predictive of experimentally determined growth rate. We used RAPS to automate the creation of 8 first draft metabolic models of new species to enable the further study of metabolism in these species.

09 BIOMASS FUELS↗
DOE OSTI2022

Dynamic Decarbonization through Autonomous Physics-Centric Deep Learning and Optimization of Building Operations (Abstract only)

This project directly addresses the primary goal of Area of Interest 2 in the CRADA call: to advance optimization-based integrated energy management systems in commercial and residential buildings. Pacific Northwest National Laboratory (PNNL) and its industry partner PassiveLogic aim to accomplish this by reaching three key objectives. First, to ensure a broad impact in the building controls industry, PNNL will extend its open-source library for predictive control synthesis by augmenting its capabilities with data-driven self-learning of building models and auto-calibration of predictive controllers. The effort will focus on building use cases selected in collaboration with PassiveLogic. The team will specifically address the development of methods for data-driven adaptation of building models, investigation of model architectures that best address specific building types, and automated synthesis of differentiable predictive controllers that optimize diverse objectives. Second, PNNL will collaborate with PassiveLogic to integrate the aforementioned methods with PasiveLogic’s advanced controls platform. The collaborative integration effort will inform the developments under the first objective by providing specific data on the attainable performance of model learning on resource-constrained edge computing platforms. This software integration effort will increase the technical maturity of the developed libraries by exploring the use of software integration tools and methods. Third, PNNL and PassiveLogic will work to improve the technology readiness of the developed predictive controllers by testing their performance in relevant test environments, such as high-fidelity simulation, hardware in the loop, and actual test buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Packaging HEP Heterogeneous Mini-apps for Portable Benchmarking and Facility Evaluation on Modern HPCs

High Energy Physics (HEP) experiments are making increasing use of GPUs and GPU dominated High Performance Computer facilities. Both the software and hardware of these systems are rapidly evolving, creating challenges for experiments to make informed decisions as to where they wish to devote resources. In its first phase, the High Energy Physics Center for Computational Excellence (HEP-CCE) produced portable versions of a number of heterogeneous HEP mini-apps, such as p2r, FastCaloSim, Patatrack and the WireCell Toolkit, that exercise a broad range of GPU characteristics, enabling cross platform and facility benchmarking and evaluation. However, these miniapps still require a significant amount of manual intervention to deploy on a new facility. We present our work in developing turn-key deployments of these mini-apps, where by means of containerization and automated configuration and build techniques such as Spack, we are able to quickly test new hardware, software, environments and entire facilities with minimal user intervention, and then track performance metrics over time.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Packaging HEP Heterogeneous Mini-apps for Portable Benchmarking and Facility Evaluation on Modern HPCs

High Energy Physics (HEP) experiments are making increasing use of GPUs and GPU dominated High Performance Computer facilities. Both the software and hardware of these systems are rapidly evolving, creating challenges for experiments to make informed decisions as to where they wish to devote resources. In its first phase, the High Energy Physics Center for Computational Excellence (HEP-CCE) produced portable versions of a number of heterogeneous HEP mini-apps, such as \ptor, FastCaloSim, Patatrack and the WireCell Toolkit, that exercise a broad range of GPU characteristics, enabling cross platform and facility benchmarking and evaluation. However, these mini-apps still require a significant amount of manual intervention to deploy on a new facility. We present our work in developing turn-key deployments of these mini-apps, where by means of containerization and automated configuration and build techniques such as Spack, we are able to quickly test new hardware, software, environments and entire facilities with minimal user intervention, and then track performance metrics over time.

Atif, Mohammad [Brookhaven] (ORCID:000000026889770↗
DOE OSTI2026

AI-Based Analytics and Energy Modeling Framework for Characterizing Urban Energy Systems

Developing location-specific district energy models is essential for understanding energy patterns and supporting efficient management and planning decisions. However, accurately characterizing these models remains challenging due to gaps in building characteristics and labor-intensive traditional modeling workflows. To address these challenges, we develop an AI-based framework that integrates top-down and bottom-up building energy data to automate urban energy model characterization. The framework trains multimodal deep learning models using heterogeneous ResStockTM datasets to infer missing building characteristics from varying levels of known information and generate simulation-ready inputs for district-scale energy modeling. It also employs a conditioning-based injection approach to generate ”what-if” scenarios, enabling users to explore retrofit, efficiency, and technology-upgrade pathways. Integrated within URBANoptTM, a bottom-up district energy modeling platform for simulating co-located buildings, the framework infers detailed building-level inputs required for bottom-up simulations. Both localized and generalized AI models are developed to learn relationships across categorical, numerical, and time-series data, enabling reconstruction of missing attributes and generation of targeted upgrade scenarios. We demonstrate this methodology on a residential neighborhood in Baltimore, MD, assessing internal consistency against ResStock reference data and URBANopt simulation, and comparing selected attributes against real-world building characteristics. Results show strong overall predictive accuracy in data completion and scenario generation, with localized and generalized models offering complementary trade-offs between precision and scalability. Overall, our automated framework streamlines energy modeling and provides a reliable framework for urban building energy characterization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Performance evaluation of automated data-driven feature extraction and selection methods for practical and scalable building energy consumption prediction models

Here, this study quantifies the impact of automated feature engineering methods (feature extraction and selection) on the quality and accuracy of machine learning models that predict building energy consumption. The case study compares model performance for three main scenarios: baseline (no feature extraction and selection), feature extraction only, and feature extraction combined with feature selection (filter and/or wrapper methods) for fully trained machine learning models for 200 metered/sub-metered energy measurements across 118 real buildings. For consistency, the same machine learning model architecture (a black box deep learning neural network with probabilistic forecast output) was used for all scenarios. Based on results, all feature engineering methods provided noticeable prediction accuracy improvements (e.g., 29%-68% median prediction improvement) compared to baseline scenarios. However, in this application, feature selection methods provide little practical value due to their limited performance gains and high computational cost. Smarter algorithm development supported by better computational environments will be needed before feature selection methods can reliably and efficiently improve predictive model performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A Knowledge-based Framework for Building Energy Model Performance Verification

Building energy modeling (BEM) has been widely used by researchers, regulators, and engineers to quantify building energy performance. Quality assurance (QA) and quality control (QC) of the model's performance are essential parts of such analysis. Currently, QA/QC is done in a manual and ad-hoc manner, which is tedious, error-prone, and time-consuming when QA/QC a large number of models. To solve these challenges, we propose a a dAta-driveN buIlding perforMance verificATion framEwork (ANIMATE), which conducts automated output-based verification of building operations requirements (especially for time-series output-based verification of control requirements). While this framework was developed for verifying energy model performance, it can be extended for other applications such as BEM software testing and performance verification of real buildings in the field.

Chen, Yan↗
DOE OSTI2022

Automated fault detection and diagnosis deployment Internet of Things solution for building energy system

The building sector is the highest energy consumer and ranks first in terms of carbon emissions among all sectors. To address these issues, decarbonization and electrification in the building energy sector are two critical missions of the new US administration. For decarbonization, the new administration has set a target to reduce greenhouse gas emissions by 50–52% by 2030 and targeting a carbon-neutral economy by 2050. For electrification, the goal is to achieve 100% clean electricity by 2035. Such decarbonization and electrification in the building sector require that energy consumption in buildings be reduced significantly. Therefore, the building sector must continuously adopt new technologies to achieve its energy and carbon emission goals. One of the most fundamental technologies is the Internet of Things (IoT). IoT has proven to be an effective solution for the building domain, including building information/energy modeling, smart buildings, etc. Although much progress has been made in the development of IoT-based building energy systems, there is still a lack of reliable, scalable, and affordable IoT-based automated fault and degradation diagnostic (AFDD) solutions. Such solutions would enable deployment of advanced algorithms into real systems to archive the projected energy benefits. Here, this study reviews existing IoT solutions developed for building energy–related application and developed a simple but effective AFDD IoT deployment solution, including developing a suitable IoT architecture and conducting easy and scalable deployment by leveraging a common cloud-based IoT service.

42 ENGINEERING↗