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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Comparing simulated demand flexibility against actual performance in commercial office buildings

Commercial building energy benchmarking has been used as a mechanism to evaluate energy use of a single building over time, relative to other similar buildings, or to simulations of a reference building conforming to various energy standards. Lack of empirical demand flexibility data and consistent flexibility metrics has limited the ability to compare demand flexibility performance with estimated demand flexibility in buildings. In this study, we collected demand response performance data for a total of 831 demand response events from 192 sites as a first step to build such a building demand flexibility dataset, and propose a standard core data schema to consolidate field data from different sources. We also performed parametric simulations of a control strategy called “global temperature adjustment” using commercial office prototype building models. We then compared the simulated demand flexibility performance against the actual data for offices with global temperature adjustment strategy implemented. During demand response events with an average outside air temperature of 34 °C (range 23 °C–42 °C), the measured demand decrease intensity of the demand flexibility metrics were 6.1 watts per square meter (W/m 2 ), 10.0 W/m 2 , 11.1 W/m 2 , 7.1 W/m 2 , and 4.7 W/m 2 for small, small–medium, medium, medium–large, and large office buildings, respectively. Compared to the measured data in medium- and large-size buildings, the simulated demand decrease intensity was 0.7 W/m 2 (17%) lower on average. The discrepancy between simulated and measured peak demand intensities fell within one standard deviation of the mean measured data. Here, the comparison results validate the credibility of simulations in capturing real building data for assessing the technical potential of building demand flexibility.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Building Performance Standards and Energy Code Alignment: Technical Brief

Building energy codes focus on building design, construction and renovation and have significantly increased building efficiency since the first national energy code was published in 1975. Most jurisdictions have energy codes based on ANSI/ASHRAE/IES Standard 90.1 (hereto referred to as Standard 90.1) and the International Energy Conservation Code (IECC). Compliance options available in these model energy codes include a prescriptive path, whole building performance paths – including IECC Total Building Performance (TBP), Standard 90.1 Energy Cost Budget (ECB) method and Performance Rating Method (PRM) – and system performance paths for envelope and heating, ventilation, and air-conditioning systems. Building performance standard (BPS) policies are an emerging policy tool used by jurisdictions to reduce the operational energy use or greenhouse gas (GHG) emissions of the existing commercial building stock. BPS policies vary widely between jurisdictions and are tailored to each location’s climate and energy goals. Intuitively, projects that met a recent edition of the energy code should comply with the BPS targets.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Developing Smart Building Technology Modules to Enhance Workforce Preparedness: A Case for AI-Driven Academic and Professional Education

Smart building technologies are resources that improve building energy efficiency and resilience, reduce carbon emissions, and provide load flexibility to the grid. However, in both academic curricula and building professionals’ continuing education, there is a lack of systematic instruction on methods to integrate multiple energy systems including distributed energy resources (DER), smart building technologies, AI (Artificial Intelligence) tools and key concepts, components, and controls, including “Internet of Things” (IoT) devices. In today’s dynamic workforce, this major gap in smart building technology education prevents stakeholders from being able to attract talent with an understanding and preparation to adopt smart building technologies in building design and operations. A federally funded project included a partnership between Slipstream and Texas A&M University (TAMU) to develop a semester-long smart building curriculum for engineering college students with the ability to adapt the contents for workforce development of professionals in building services. The final product consists of 16 training videos adapted for building professionals and the public. The educational content and training materials cover the benefits of building energy systems, the latest sensor technologies and IoT devices, all with a focus on smart building technologies. The key drivers are on topics related to smart building controls (i.e., energy management information systems), smart building control platforms, cybersecurity, grid-interactive-efficient buildings (GEBs), smart building control methods, and occupant-centric control. Although not explicitly included the technologies nod to the need for AI driven technologies to prepare engineers and industry professionals to be future ready. This paper describes the project approach, provides outlines of the training materials, and identifies lessons learned in creating the content for this course. The authors suggest ways to scale the instruction of smart building concepts to empower the workforce to accelerate the adoption of smart building technologies and AI-based teaching and learning in higher education and building sector.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

High‐Performance Low‐Emissivity Paints Enabled by N‐Doped Poly(benzodifurandione) (n‐PBDF) for Energy‐Efficient Buildings

Abstract Low‐emissivity (low‐e) paints reduce radiative heat exchange between buildings and the environment, stabilizing indoor climates and lowering air conditioning demand. However, low‐cost, durable, and colored low‐e paints have yet to be demonstrated. Here, an approach is proposed using n‐doped poly(benzodifurandione) (n‐PBDF), a transparent organic conducting polymer, coated over colored commercial paints. This achieves a low thermal emissivity of 0.19 in the mid‐infrared spectrum, attributed to the efficient charge transport of delocalized π‐electrons in n‐PBDF structure. The reduction in thermal emissivity aids in regulating building temperatures by minimizing heat transfer between buildings and their surroundings across diverse climate zones and seasons. The n‐PBDF coating preserves the underlying paint's color due to its high visible transparency, meeting aesthetic requirements. It also shows strong stability in accelerated indoor weathering tests, ensuring long‐term performance. Simulations estimate annual HVAC energy savings of over 10,800 kWh in San Francisco and 5,500 kWh in Chicago for the typical mid‐rise apartments. The paint's versatility, scalability, and durability make it suitable for buildings, vehicles, and greenhouses, aiding urban heat island mitigation.

Liu, Xiaojie [School of Mechanical Engineering and↗
DOE OSTI2020

Recent Experiences Calculating g-functions for Use in Simulation of Ground Heat Exchangers

Temperature response functions, known as g-functions, are a computationally efficient method for simulating ground heat exchangers (GHEs), used with ground-source heat pump (GSHP) systems, either as part of a whole-building energy simulation or as part of a dedicated ground heat exchanger design tool. In fact, at present, they are the only feasible way to simulate a ground-source heat pump system in a whole-building energy simulation. This paper summarizes recent developments in the field and recent experience using a new open-source g-function calculation tool known as pygfunction (Cimmino 2018a, 2018b). This experience includes accuracy, computation time, memory requirements and sensitivity to boundary conditions. With larger ground heat exchangers, e.g. in excess of 100 boreholes, the computational time and memory requirements can create challenges.

Spitler, Jeffery↗
DOE OSTI2024

Smart Building Technology Training Modules for Academic and Professional Education

Smart building technologies are a new suite of resources that improve building energy efficiency and resilience, reduce carbon emissions, and provide load flexibility to the grid. However, in both college curricula and building professionals’ continuing education, there is a lack of systematic instruction on smart building technologies–topics that include smart building concepts, key components, smart building controls, “Internet of Things” (IoT) devices, and how to integrate multiple energy systems including distributed energy resources (DER). This major gap in smart building education prevents stakeholders from understanding and adopting smart building technologies in building design and operations. Slipstream leads a DOE-funded project developing a semester-long smart building curriculum for college students and adapting the contents into 16 training videos for building professionals and the general public. The education and training cover the drivers and benefits of smart building technologies, key building energy systems, the latest sensor technologies and IoT devices, and focus on topics related to smart building controls (i.e., energy management information systems, smart building control platforms, cybersecurity, grid-interactive-efficient buildings (GEBs), smart building control methods, and occupant-centric control. This paper describes the project approach, provides outlines of the training materials, and identifies lessons learned in creating the content. We also suggest ways to scale the instruction of smart building concepts to empower the workforce to accelerate the adoption of smart building technologies in the real world.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

OpenStudio®-MCP [SWR-26-035]

OpenStudio®-MCP is a Model Context Protocol (MCP) server that lets AI assistants perform building energy modeling through natural language. Rather than requiring users to learn the OpenStudio® SDK, EnergyPlus® scripting, or Ruby/Python automation, the server translates conversational requests into sequences of tool calls that create models, design HVAC systems, run simulations, and extract results — all within a single chat session. The server's 124 tools are organized into a skills architecture where each skill encapsulates a domain of building energy modeling (envelope, HVAC, loads, weather, simulation, results) behind typed, LLM-friendly interfaces. High-leverage operations like applying ASHRAE 90.1 baseline systems or generating standards-compliant typical buildings are exposed as single tool calls that internally wire dozens of OpenStudio® objects. Bundled measures from ComStock™ and Openstudio® -common-measures-gem are wrapped with dedicated tools and typed arguments rather than exposed through a generic measure interface, so AI models get consistent, error-resistant recipes without needing to discover measure arguments at runtime. A key design decision is structured results extraction: six SQL-based tools return surgical ~300–1,000 token responses (end-use breakdowns, envelope summaries, HVAC sizing, timeseries data) instead of requiring the AI to parse ~100K-token raw HTML reports, making iterative design exploration practical within context window limits. The codebase is designed as a reference implementation — explicit, well-commented, and modular — so that other simulation engines (EnergyPlus® standalone, TRNSYS, DOE-2) can use it as a template for building their own MCP servers.

Ball, Brian [National Laboratory of the Rockies (N↗
DOE OSTI2022

Review of the U.S. Policies, Codes, and Standards of Zero-Carbon Buildings

The global issue of climate change has accelerated the international commitment to net-zero carbon emission development. Decarbonizing the building sector has been put on several governments’ sustainable development agendas. To provide a reference for decarbonizing the building sector, this paper summarizes the U.S. experience in zero-carbon buildings (ZCBs) from the aspects of policies, codes, and standards at the federal and local levels and those of professional societies. Based on the definition and boundaries of ZCBs, this paper introduces policies on building energy efficiency, electrification, on-site renewable energy deployment, and “buy clean”, illustrating highlights in building phases, energy systems, materials production, and fiscal incentives. The synergic efforts and coordination between federal and local levels and with professional societies are also introduced. Successful experiences in policy and standard implementation are summarized, including the systemic work of multilevel governance, clearly defined goals and stringent policies, constant upgrades of codes and standards, transparency in reporting and information sharing, and increased financial and investment opportunities. This paper provides concrete recommendations for developing zero-carbon building policies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Open-cycle thermochemical energy storage for building space heating: Practical system configurations and effective energy density

Salt-hydrate thermochemical materials (TCM) are promising candidates for energy storage systems for building space heating due to their high theoretical energy density and the need for low regeneration temperature. However, water vapor is required to drive the hydration process of the TCM reactor, which poses a challenge during winter when water vapor is typically scarce. Using indoor air directly lowers the building's humidity to an unconformable level in practice, while the cold outdoor air contains limited moisture. Here we consider different integration strategies for open-cycle TCM reactors in buildings and develop a model to simulate their thermal performance across diverse buildings and climates, specifically for building space heating. The potential energy densities and the levelized cost of storage of the TCM reactor are evaluated in practical scenarios to demonstrate the load-shifting potential of TCM systems for heating applications. We use a strontium chloride (SrCl 2 )-based composite as the baseline and explore the impact of various reactor and material changes to the energy density and levelized cost of storage.

25 ENERGY STORAGE↗
DOE OSTI2020

Perspectives on the FESAC transformative enabling capabilities: Priorities, plans, and Status

In early 2017, the Fusion Energy Sciences Advisory Committee (FESAC), an advisory committee to the United States Department of Energy, was charged with identifying transformative enabling capabilities (TECs) “that could promote efficient advance toward fusion energy, building on burning plasma science and technology.” A subcommittee with broad expertise was formed and sought feedback from scientific experts, including experts from outside the fusion community. Three workshops were conducted, and a report was approved by FESAC in 2018 that identified four of the “most promising” TECs: advanced algorithms, high-critical-temperature superconductors, advanced materials and manufacturing, and novel technologies for tritium fuel cycle control. In addition, one “promising” TEC was identified: fast flowing liquid metal plasma-facing components. This paper will give details on the promising TECs and an overview on considerations of these TECs in the United States since the publication of the report.

Lumsdaine, Arnold↗
DOE OSTI2022

3D Reality Energy Modeling Software

The team will combine Bentley Systems’ ContextCapture and extend Oak Ridge National Laboratory’s building energy modeling capabilities in order to create digital twins of buildings that allow simulation-informed improvement for energy efficiency and demand response in the design and operation of the built environment. Bentley Systems’ ContextCapture software processes data from 3D laser scanning and photographs (through photogrammetry) to create a photorealistic, 3D mesh with a scale of individual objects to building to city-sized. This capability easily provides city-level visualization models and platforms where sustainable design alternatives are readily evaluated. Oak Ridge National Laboratory (ORNL) serves the U.S. Dept. of Energy (DOE) as one of three core laboratories developing building energy modeling tools EnergyPlus and OpenStudio. ORNL’s AutoBEM software (bit.ly/AutoBEM) can process imagery (satellite, aerial, and street level), LiDAR, cartographic layers, tax assessor’s data, and other data sources to extract building footprints, height, window-to-wall ratio, building type, vintage, and other building properties. AutoBEM has created 178,368 building energy models that were empirically validated with 15-minute whole-building electrical data from a utility.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1976

Spectroscopic far ultraviolet observations of transition zone instabilities and their possible role in a pre-flare energy build-up

Highly flare-productive newly emerging active regions are characterized by numerous small low-lying loops which frequently show a chaotic pattern. Flare activity in such a region subsides as the chaotic loop structures relax and expand into a bipolar configuration. The transition zone in such an active region is highly unstable, as shown by broadened and shifted nonthermal line profiles of medium-ionized elements like Si III, Si IV, and C IV. These transition-zone instabilities, which occur as isolated events in active regions of low flare productivity, are often observed prior to flares. Transition-zone instabilities can be traced to the footpoints of active loops and seem to be accompanied by heating of the loop. The loops vary in size and show differing degrees of activity, with the brightest and most compact ones seemingly being in a pre-flare state which results in the catastrophic energy release along the loop during a flare.

Brueckner, G. E.↗
NASA NTRS1976

Building application of solar energy. Study no. 4: Scenarios for the utilization of solar energy in southern California buildings, change 1

Plausible future market scenarios for solar heating and cooling systems into buildings in the area served by the Southern California Edison Company. A range of plausible estimates for the number of solar systems which might be installed and the electrical energy which might be displaced by energy from these systems are provided. The effect on peak electrical load was not explicitly calculated but preliminary conclusions concerning peak load can be inferred from the estimates presented. Two markets are investigated: the single family market and the large power commercial market.

Davis, E. S.↗
DOE OSTI2020

Exploration of Domain Aware Machine Learning for Grid Analytics: Transfer-Learnt Energy Models to Assist Buildings Control with Sparse Field Data

Buildings are a primary consumer of energy in the United States and are also increasingly being perceived as providers of grid services such as load shifting, shedding and modulation. High fidelity models of building energy consumption are needed to set appropriate baselines for measurement and verification (M&V) of controllers designed for energy efficient operation of buildings and to enable buildings to provide grid services via. participation in demand response programs. State-of-the-art building energy modeling techniques either rely on Physics based models, or extensive instrumentation of the building envelope to gather “big” data to train machine learning based models such as deep neural networks. While Physics based models are often limited by their accuracy, it is not always feasible to gather a significant amount of field data required to train machine learning based models with sufficient accuracy. In this paper, we explore the use of transfer learning-based strategies to address unsatisfactory accuracy of models for estimating building energy consumption when available field data for training is sparse or of unacceptable quality. In particular, we transfer knowledge in the form of data and parameters, from Physics based simulation frameworks to the field to improve the model accuracy, thus resulting in a Physics-informed Machine Learning framework. We evaluated the efficacy of our approach on field data collected from six commercial buildings and our results indicate that the proposed transfer learning based models provide comparative (and in some cases better) accuracy than state-of-the-art machine learning and deep learning solutions, with just one month of field data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

City-level impacts of building tune-ups: Findings from Seattle's building tune-ups program

Many U.S. cities are implementing policies to reduce greenhouse gas (GHG) emissions of their buildings. These range from building energy benchmarking and disclosure to building performance standards (BPS) that require buildings to meet specific targets of energy use or emissions. The City of Seattle adopted a climate action plan in 2013 that set a goal of zero net GHG emissions in the road transportation, buildings, and waste sectors by 2050, with a number of near and long term actions. Seattle implemented mandatory building tune-ups in 2016, applying to commercial buildings larger than 50,000 sqft. Building tune-ups1 involve assessment and implementation of operational and maintenance (O + M) improvements to achieve energy and water efficiency, such as changes to thermostat set points or adjusting lighting or irrigation schedules. Seattle's tune-ups program covered 27 such improvements in HVAC, lighting, domestic hot water, and envelope systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Anomaly Attribution with Likelihood Compensation

This paper addresses the task of explaining anomalous predictions of a black-box regression model. When using a black-box model, such as one to predict building energy consumption from many sensor measurements, we often have a situation where some observed samples may significantly deviate from their prediction. It may be due to a sub-optimal black-box model, or simply because those samples are outliers. In either case, one would ideally want to compute a responsibility score indicative of the extent to which an input variable is responsible for the anomalous output. In this work, we formalize this task as a statistical inverse problem: Given model deviation from the expected value, infer the responsibility score of each of the input variables. We propose a new method called likelihood compensation (LC), which is founded on the likelihood principle and computes a correction to each input variable. To the best of our knowledge, this is the first principled framework that computes a responsibility score for real valued anomalous model deviations. We apply our approach to a real-world building energy prediction task and confirm its utility based on expert feedback.

Idé, Tsuyoshi↗
DOE OSTI2022

Deep reinforcement learning with online data augmentation to improve sample efficiency for intelligent HVAC control

Deep Reinforcement Learning (DRL) has started showing success in real-world applications such as building energy optimization. Much of the research in this space utilized simulated environments to train RL-agent in an offline mode. Very few research have used DRL-based control in real-world systems due to two main reasons: 1) sample efficiency challenge---DRL approaches need to perform a lot of interactions with the environment to collect sufficient experiences to learn from, which is difficult in real systems, and 2) comfort or safety related constraints---user's comfort must never or at least rarely be violated. In this work, we propose a novel deep Reinforcement Learning framework with online Data Augmentation (RLDA) to address the sample efficiency challenge of real-world RL. We used a time series Generative Adversarial Network (TimeGAN) architecture as a data generator. We further evaluated the proposed RLDA framework using a case study of an intelligent HVAC control. With a ≈28% improvement in the sample efficiency, RLDA framework lays the way towards increased adoption of DRL-based intelligent control in real-world building energy management systems.

Kurte, Kuldeep↗
DOE OSTI2024

Pushing the Envelope-Moving Dynamic Building Envelope Thermal Energy Storage Systems Mainstream: Preprint

Buildings contribute to nearly 40% of the U.S. national energy consumption and a significant portion of CO2 emissions. More importantly, disadvantaged communities are disproportionately affected by energy burden and thermal discomfort in their homes. This paper will discuss two novel DOE's BTO supported thermal energy storage (TES) integrated dynamic building envelope technologies, their ability to harvest ambient energy, and improve energy efficiency by reducing HVAC loads and peak electricity demand while enhancing energy and thermal resilience in buildings. The paper will also discuss the recent advancements that have made these systems more affordable and easier to integrate into new and existing buildings. The first solution is a thermally anisotropic building envelope (TABE) system that can redirect ambient thermal energy (heat or coolness) from diurnal outdoor conditions, solar irradiance, and night sky radiation from the envelope to a hydronic loop. The redirected thermal energy can be stored in a TABE-integrated thermal energy storage system and use the stored energy to offset HVAC energy use and peak demand. The second solution is an innovative plug-and-play thermal switch in the form of insertable plugs integrated with a phase change material (PCM). The plug can vary its thermal resistance based on the indoor and outdoor conditions, thus allowing preferential directional heat flow, and enhancing utilization of free ambient cooling and heating to charge/discharge the PCM, much like a solid-state economizer. While the fist solution can be actively controlled, the second solution is passive, requiring no external power, and work solely based on the ambient temperature.

anisotropic envelope↗