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At least 127 records · Page 7

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DOE OSTI2024

Building ControlScore: General Service Administration Office Building Deployment

Improvements to building control systems can lead to energy savings and increased occupant comfort. In an optimized system, process variables such as air temperature will closely follow their desired setpoints and avoid excess energy use. Typically, experts must manually inspect individual control loops to identify poor performance and opportunities for improvement. However, this approach is difficult in modern buildings that have a prohibitively large number of controllers. To address this issue, Pacific Northwest National Laboratory (PNNL) created the ControlScore concept, which takes operating data from the many controllers within a building and generates standardized scores for each loop on a scale of 0 to 10 (a score of 0 indicates poor control, a score of 10 indicates good control). PNNL applied the Building ControlScore application to all available data from a General Services Administration office building within the period of January 1, 2023, to March 9, 2023. The building scored a 4.7 overall, with all 74 of the building’s loops fitting a roughly normal distribution centered around 5. These results indicate that the analyzed systems have below-average performance with room for improvement, especially in the poorly scored systems. Airflow loops tended to have much lower scores than zone temperature loops. The lowest and highest performing systems in the building section were identified, as were all loops with a score less than 1. While the ControlScore identifies loops and systems that aren’t meeting their designated setpoints, it does not indicate the cause of those issues. For example, consider a supply air terminal unit’s airflow loop that received a low score due to it delivering less air than specified by the setpoint. The lower-than-desired airflow could be due to equipment limitations (e.g., the terminal unit or duct serving is too small to accommodate that airflow), malfunctioning equipment (e.g., a stuck damper or bad sensor), or something else entirely. The ControlScore does not diagnose problems it simply identifies the symptoms that can be explored and addressed by building operators.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Machine learning-enhanced hybrid modeling approach for better identification of a building thermal network model and improved prediction

The gray-box modeling approach, which uses a semi-physical thermal network model, has been widely used in building prediction applications, such as model predictive control (MPC). However, unmeasured disturbances, such as occupants, lighting, and in/exfiltration loads, make it challenging to apply this approach to practical buildings. In this word, we propose a hybrid modeling approach that integrates the gray-box model with a model for unmeasured disturbance. After reviewing several system identification approaches, we systematically designed the unmeasured disturbance model with a model selection process based on statistical tests to make it robust. We generated data based on the building model calibrated by real operational data and then trained the hybrid model for two different weather conditions. The hybrid model approach demonstrates an RMSE reduction of approximately 0.2–0.9 °C and 0.3–2 °C on 1-day ahead temperature prediction compared to the Conventional approach for mild (Berkeley, CA) and cold (Chicago, IL) climates, respectively. In addition, this approach was applied to experimental data obtained from the laboratory building to be used for the MPC application, showing superior prediction performances.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Design and Performance of Kokkos Staging Space toward Scalable Resilient Application Couplings

With the growing number of applications designed for heterogeneous HPC devices, application programmers and users are finding it challenging to compose scalable workflows as ensembles of these applications, that are portable, performant and resilient. The Kokkos C++ library has been designed to simplify this cumbersome procedure by providing an intra-application uniform programming model and portable performance. However, assembling multiple Kokkos-enabled applications into a complex workflow is still a challenge. Although Kokkos enables a uniform programming model, the inter-application data exchange still remains a challenge from both performance and software development cost perspectives. In order to address this issue, we propose Kokkos data staging memory space, an extension of Kokkos' data abstraction (memory space) for heterogeneous computing systems. This new abstraction allows to express data on a virtual shared-space for multiple Kokkos applications, thus extending Kokkos to support inter-application data exchange to build an efficient application workflow. Additionally, we study the effectiveness of asynchronous data layout conversions for applications requiring different memory access patterns for the shared data. Our preliminary evaluation with a synthetic benchmark indicate the effectiveness of this conversion adapted to three different scenarios representing access frequency and use patterns of the shared data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Resilience Issues in Building Energy Codes

This report, prepared for the International Energy Agency’s Building Energy Codes Working Group (BECWG, part of the IEA Energy in Buildings and Communities Technical Collaboration Programme, or IEA EBC), focuses on the application of building energy codes to improve the ability of buildings to provide a minimum healthy level of thermal comfort and function during extreme events. It reviews the relationship of building energy codes to the other building resilience policies and strategies, such as other building life/safety codes, community planning or zoning to discourage rebuilding in areas most prone to climate disasters, and other resilience planning strategies. The report provides an overview of how different jurisdictions address resilience issues in building energy codes in countries that are part of the IEA EBC Building Energy Codes Working Group.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Automatic building energy model development and debugging using large language models agentic workflow

Building energy modeling (BEM) is a complex process that demands significant time and expertise, limiting its broader application in building design and operations. While Large Language Models (LLMs) agentic workflow have facilitated complex engineering processes, their application in BEM has not been specifically explored. This paper investigates the feasibility of automating BEM using LLM agentic workflow. Here, we developed a generic LLM-planning-based workflow that takes a building description as input and generates an error-free EnergyPlus building energy model. Our robust workflow includes four core agents: 1) Building Description Pre-Processing, 2) IDF Object Information Extraction, 3) Single IDF Object Generator Suite, and 4) IDF Debugging Agent. These agents divide the complex tasks into manageable sub-steps, enabling LLMs to generate accurate and reliable results at each stage. The case study demonstrates the successful translation of a building description into an error-free EnergyPlus model for the iUnit modular building at the National Renewable Energy Laboratory. The effectiveness of our workflow surpasses: 1) naive prompt engineering, 2) other LLM-based workflows, and 3) manual modeling, in terms of accuracy, reliability, and time efficiency. The paper concludes with a discussion on the interplay between foundational models and LLM agent planning design, advocating for the use of fine-tuned, specialized models to advance this field.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Building Efficiency Targeting Tool for Energy Retrofits (BETTER) Web Application (BETTER Web App) v1.0

The Building Efficiency Targeting Tool for Energy Retrofits (BETTER) V.1.0 web application identifies cost-saving energy and emissions reductions in buildings and portfolios, without site visits or complex modeling. With minimal data entry, BETTER benchmarks a building's or portfolio's energy use against peers; quantifies energy, cost, and greenhouse gas (GHG) reduction potential; and recommends energy efficiency measures for individual buildings or portfolios, targeting specific energy savings levels. The source code of BETTER's modular, cross-platform analytical engine has previously been disclosed (2019-001) and is available on GitHub and can be adopted, redeveloped, and redistributed freely under an open-source license, allowing users to incorporate BETTER's analytical capabilities into their own software platforms and tools. The BETTER V.1.0 web application, developed with the Django web-framework and the Model-View-Controller (MVC) architecture being disclosed here, provides a graphical user interface (GUI) for any user to view the software tutorials, download and upload a data entry template, configure and run the BETTER analyses, and view the final analytical reports.

Szum, Carolyn↗

DOE OSTI2019

Typical occupancy profiles and behaviors in residential buildings in the United States

The energy performance of a residential building is highly dependent on occupant's presence or non-presence in a building and their interactions with energy-consuming appliances. Typical occupancy schedules for residential buildings must be defined for applications such as building energy modeling as well as for assessing energy savings associated with the use of occupancy sensing technologies and occupancy-dependent controls. Currently, commonly used simulation programs assume a typical occupancy schedule, however, there is significant opportunity for improvement to these schedules as this is generally based on engineering judgement. This research uses 12 years of the American Time Use Survey (ATUS) data to develop typical occupancy schedules for a range of household types and occupant age ranges. This is compared to currently utilized residential occupancy schedules. In many cases the developed schedules exhibit similar patterns, however, differences are also found to be as high as 41% for certain periods of time. For occupancy sensing applications, the spatial-temporal distribution of occupants in residential buildings is also evaluated. These locations vary based on temporal factors as well as demographic factors such as age and number of occupants. Finally, the results of this research work towards improved occupancy schedule development can benefit both industry professionals and researchers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Energy cost savings and expected payback for Re-tuning the controls of US Army buildings

The Headquarters Department of the Army (HQDA) sponsored a study to determine the national impact of deploying the Re-tuning™ methodology in 5 buildings types that account for over 40% of the Army’s conditioned building stock. Re-tuning is a systematic process that improves operational efficiency and reduces energy consumption at no- or low-cost through the building automation system (BAS) by correcting operational problems that plague buildings. The study relied on successful demonstration of the re-tuning methodology at 4 pilot US Army installations that informed a holistic effort that included simulating 12 individual re-tuning energy efficiency measures (EEMs) and 6 packages of EEMs in 5 selected Army building prototype models that represent 311 million ft2 (28.9 million m2) of the Army’s conditioned floor space. Both the baseline buildings and the packages were customized to capture the expected outcomes of re-tuning a diverse set of buildings. The study highlighted the benefit of individual re-tuning EEMs and economics of implementing packages of EEMs in applicable Army buildings across 16 climates and 2 building vintages. The average whole-building energy savings ranged from 10.8% to 40.5% by building type, with the company operations facility (COF) building having the highest value proposition from re-tuning. In addition, the study reveals that all large office (LO) buildings in the Army are economical to re-tune. The total modeled cost savings potential for re-tuning the five building types across the US Army is $204/1,000 sf ($220/100 m2), or $64M annually. This cost savings represents 5.6% of all the Army’s energy expenditures.

Fernandez, Nicholas EP↗

DOE OSTI2021

A synthetic building operation dataset

Abstract This paper presents a synthetic building operation dataset which includes HVAC, lighting, miscellaneous electric loads (MELs) system operating conditions, occupant counts, environmental parameters, end-use and whole-building energy consumptions at 10-minute intervals. The data is created with 1395 annual simulations using the U.S. DOE detailed medium-sized reference office building, and 30 years’ historical weather data in three typical climates including Miami, San Francisco, and Chicago. Three energy efficiency levels of the building and systems are considered. Assumptions regarding occupant movements, occupants’ diverse temperature preferences, lighting, and MELs are adopted to reflect realistic building operations. A semantic building metadata schema - BRICK, is used to store the building metadata. The dataset is saved in a 1.2 TB of compressed HDF5 file. This dataset can be used in various applications, including building energy and load shape benchmarking, energy model calibration, evaluation of occupant and weather variability and their influences on building performance, algorithm development and testing for thermal and energy load prediction, model predictive control, policy development for reinforcement learning based building controls.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

AlphaBuilding - Synthetic Buildings Operation Dataset

This is a synthetic building operation dataset which includes HVAC, lighting, miscellaneous electric loads (MELs) system operating conditions, occupant counts, environmental parameters, end-use and whole-building energy consumptions at 10-minute intervals. The data is created with 1395 annual simulations using the U.S. DOE detailed medium-sized reference office building, and 30 years' historical weather data in three typical climates including Miami, San Francisco, and Chicago. Three energy efficiency levels of the building and systems are considered. Assumptions regarding occupant movements, occupants' diverse temperature preferences, lighting, and MELs are adopted to reflect realistic building operations. A semantic building metadata schema - BRICK, is used to store the building metadata. The dataset is saved in a 1.2 TB of compressed HDF5 file. This dataset can be used in various applications, including building energy and load shape benchmarking, energy model calibration, evaluation of occupant and weather variability and their influences on building performance, algorithm development and testing for thermal and energy load prediction, model predictive control, policy development for reinforcement learning based building controls.

AlphaBuilding↗

DOE OSTI2021

Empirical validation and comparison of methodologies to simulate micro and macro-encapsulated PCMs in the building envelope

Thermal Energy Storage (TES) has the potential to shift peak electricity demand. Passive TES is usually implemented in building envelope as micro and macro encapsulated phase change materials (PCM) to shift electric energy demand and therefore requires careful heat transfer analysis. Whole building energy modelling with simplified heat transfer analysis has become extremely important for designers, architects, engineers, and researchers to predict energy performance of buildings. It is important to validate PCM modelling algorithms used in building energy programs to quantify their error and prove their capacity to model different PCM encapsulation types. This study uses data from a microencapsulated PCM and two macroencapsulated PCMs (Bio based PCM and hydrate salts) tested in full-scale using the Advanced Multiscale Building Energy Research (AMBER) Lab located at the Colorado School of Mines and is used to validate a numerical algorithm written in MATLAB language. To approximate the heat transfer through a wall assembly with macroencapsulated PCM pouches, several modelling techniques that can reduce 3D heat transfer characteristics to 1D are explored in this research. A parallel path heat transfer modelling approach is found to give the closest agreement with the experimental data for the pouched PCMs in building envelope applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Redesign of the Timeline Generator at Fermilab using a web-based Flutter application, GraphQL API and an IOC

The control system at Fermilab is undergoing an evolution with a shift towards web-based applications with connections to the EPICS infrastructure. The Timeline Generator (TLG) is an application that serves to coordinate events across the lab using different timing links. These links include the Tevatron clock (TCLK), a 10 MHz serial link with events encoded at 20Hz and Machine Data (MDAT), a communication link with states encoded at 720Hz. This paper covers the redesign of the major components of the TLG. This includes a web-based Flutter application for building timelines. A placement service is in use that has a GraphQL interface and uses a timeline input to compute a schedule of events and states. The Flutter application sends this computed schedule to the TLG IOC via a GraphQL interface to the Data Pool Manager (DPM). The TLG IOC runs on an Arria FPGA, the Accelerator Clock Generator (ACLK-GEN), which is responsible for writing the events and states on to the different timing links.

Carmichael, Linden [Fermilab]↗

DOE OSTI2025

Redesign of the Timeline Generator at Fermilab using a web-based Flutter Application, GraphQL API and an IOC

Redesign of the Timeline Generator at Fermilab using a web-based Flutter application, GraphQL API and an IOC ABSTRACT = The control system at Fermilab is undergoing an evolution with a shift towards web-based applications with connections to the EPICS infrastructure. The Timeline Generator (TLG) is an application that serves to coordinate events across the lab using different timing links. These links include the Tevatron clock (TCLK), a 10 MHz serial link with events encoded at 20Hz and Ma-chine Data (MDAT), a communication link with states encoded at 720Hz. This paper covers the redesign of the major components of the TLG. This includes a web-based Flutter application for building timelines. A placement service is in use that has a GraphQL interface and uses a timeline input to compute a schedule of events and states. The Flutter application sends this computed schedule to the TLG IOC via a GraphQL interface to the Data Pool Manager (DPM). The TLG IOC runs on an Arria FPGA, the Accelerator Clock Generator (ACLK-GEN), which is responsible for writing the events and states on to the different timing links.

Carmichael, Linden [Fermilab]↗

DOE OSTI2023

ParFlow Sand Tank: A tool for groundwater exploration

The ParFlow Sand Tank model is an open source application designed to allow users to interactively simulate and visualize groundwater movement through the subsurface. The app is designed for both research and education; teaching hydrogeology concepts and making it easy explore and run sophisticated groundwater simulations. Our goal is to support increased accessibility and usability of research grade hydrology tools for research and teaching. The Sand Tank application simulates groundwater and surface water fluxes as well as contaminant transport in real time using the integrated physical hydrology model ParFlow (Kollet & Maxwell, 2006; Maxwell & Miller, 2005; Osei-Kuffuor et al., 2014) and the particle tracking code EcoSlim (Maxwell et al., 2019). ParFlow is a numerical hydrology model that simulates spatially distributed groundwater and surface water flow. It is a well established research tool with more than 90 publications documenting its development use to advance our understanding of groundwater dynamics and groundwater surface water interactions from the hillslope to the continental scale e.g. (Condon et al., 2020; Condon & Maxwell, 2019; Maxwell & Condon, 2016). It is designed for efficient parallel computation and has been run on many platforms spanning from laptops to supercomputers. However, one of the challenges of ParFlow is that it requires significant training and hydrologic expertise to develop simulations. The Sand Tank application makes this model accessible to anyone for education and exploration. Our application uses ParFlow for its simulation backend and ParaView for the data loading and processing. The communication infrastructure relies on the ParaViewWeb framework. We use model templates deployed in Docker images to setup the Sand Tank framework. Users can build the application locally or interact with it through our web deployment. When interacting with a template users can interactively change model parameters like subsurface processes or pump/inject water into the subsurface and watch the system respond to their changes in real time as the simulation runs. Additionally, our template setup will allow more advanced users to build custom templates of increasing complexity for both research and educational purposes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Hamilton: Flexible, Open Source $10 Wireless Sensor System for Energy Efficient Building Operation

Sensors for improving building performance are rapidly populating the market, driven in part by the drive to reduce greenhouse gas emissions resulting from energy production as well as improve the interior environment for healthy and more productive spaces. UC Berkeley has led wireless sensor development over the past 25 years (e.g., Telos mote), with the Hamilton (named after Alexander Hamilton on the US $10 bill) as the most recent. The Hamilton sensor was designed as a low-cost high-performance sensor that is modular and interoperable. The objective of the Hamilton project was to create, evaluate and establish the technological foundations for secure and easy to deploy building energy efficiency applications utilizing pervasive, low-cost wireless sensors integrated with traditional Building Management Systems (BMS), consumer-sector building components, and powerful data analytics. The project included iterative hardware design, incorporating a high-performance database (BTrDb, http://btrdb.io/), creating and iterating the development of secure data middleware (BOSSwave, WAVE/WAVEMQ), working with and pushing the development of an open-source tiny operating system RiotOS, and implementing and improving protocols such as Thread/OpenThread and TCP/IP. The hardware benefited from careful design to drive down the cost; the design included a System-on-a-Chip (SoC), chip antenna, single crystal and five passive components. Careful design of the operating system created a low-power design to enable a long life with small batteries. The hardware included several sensors: temperature, radiant temperature, relative humidity, magnetometer, accelerometer, and light, with an optional occupancy (Passive InfraRed) sensor. The project was the basis of several applications, both internal to the research team and other researchers and professionals at other institutions. Several applications used the sensor hardware as the basis for other complex devices. Other applications used the sensors to improve building performance through interoperating with the building Heating Ventilation and Air-Conditioning (HVAC) system, such as using occupancy and/or distributed temperature sensing to reduce HVAC zone energy while still providing thermal comfort and to reduce peak loads in small commercial buildings. We demonstrated cloud-based energy analytics, implemented a schedule and a Model Predictive Controller in a small commercial building to optimize HVAC energy, occupancy and electricity price. Initial integration of these technological innovations was performed through the creation of execution containers containing the WAVE agent and various driver, proxy, or building system function logic. The research added to the understanding of efficient sensor hardware, secure middleware, time-series data management (high performance database), efficient communication protocols, and interoperating with applications and building systems. The project showed the technical effectiveness and economic feasibility of creating a low-cost, modular, and easy-to-deploy sensor. Through conversations with multiple end users, the research team discovered that many customers wanted data management and services in addition to the sensors. HamiltonIOT developed packages of sensors, border router, and data services to provide a seamless “plug-and-play” sensor deployment. Some customers were willing to pay for higher quality sensors (such as light); some customers wanted a robust enclosure (waterproof).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Working with Multiple MPIs: Overcoming ABI Incompatibility

Scientific applications rely on third-party libraries, which may have multiple implementations. While sharing an application programming interface (API), many of these implementations do not have a shared application binary interface (ABI) and require recompiling. Recompiling can be a long and complex process and sometimes not even an option when the application is shipped binary only. ABI incompatibility strikes at the heart of portability, productivity, and performance by (1) impeding application execution across different HPC and Cloud systems; (2) adding developer hours rebuilding an application; and (3) not taking advantage of host-optimized libraries. This tutorial teaches attendees a portable way to address ABI incompatibility in MPI using the Wi4MPI library. Wi4MPI translates the ABI dynamically from the MPI library used to build the application to a different MPI library available at run time. With Wi4MPI, HPC practitioners can break the portability barrier imposed by ABI incompatibility, potentially increase performance, and increase user productivity. The tutorial is broken down in three components: (1) Understanding ABI compatibility in MPI; (2) Translating MPI libraries dynamically; and (3) Applying dynamic translation to key use cases in HPC, including Containers. If you use more than one MPI library or supercomputer, this tutorial is for you.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy systems

Deep Reinforcement Learning (DRL)-based control shows enhanced performance in the management of integrated energy systems when compared with Rule-Based Controllers (RBCs), but it still lacks scalability and generalisation due to the necessity of using tailored models for the training process. Transfer Learning (TL) is a potential solution to address this limitation. However, existing TL applications in building control have been mostly tested among buildings with similar features, not addressing the need to scale up advanced control in real-world scenarios with diverse energy systems. This paper assesses the performance of an online heterogeneous TL strategy, comparing it with RBC and offline and online DRL controllers in a simulation setup using EnergyPlus and Python. The study tests the transfer in both transductive and inductive settings of a DRL policy designed to manage a chiller coupled with a Thermal Energy Storage (TES). The control policy is pre-trained on a source building and transferred to various target buildings characterised by an integrated energy system including photovoltaic and battery energy storage systems, different building envelope features, occupancy schedule and boundary conditions (e.g., weather and price signal). The TL approach incorporates model slicing, imitation learning and fine-tuning to handle diverse state spaces and reward functions between source and target buildings. Results show that the proposed methodology leads to a reduction of 10% in electricity cost and between 10% and 40% in the mean value of the daily average temperature violation rate compared to RBC and online DRL controllers. Moreover, online TL maximises self-sufficiency and self-consumption by 9% and 11% with respect to RBC. Conversely, online TL achieves worse performance compared to offline DRL in either transductive or inductive settings. However, offline Deep Reinforcement Learning (DRL) agents should be trained at least for 15 episodes to reach the same level of performance as the online TL. Therefore, the proposed online TL methodology is effective, completely model-free and it can be directly implemented in real buildings with satisfying performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Integrated Renewable Energy Systems

Pacific Northwest National Laboratory (PNNL) operates the Department of Energy’s (DOE) only dedicated marine laboratory at the PNNL-Sequim campus. PNNL is leading research in the blue economy and marine energy applications and building collaboration between DOE and multiple partners in the state of Washington and beyond. With Washington State support, the Integrated Renewable Energy System (IRES) demonstration testbed proposed here will advance research by developing and testing renewable energy production, management, and use for multiple marine applications (e.g., ocean observations, underwater vehicles, aquaculture). It will also advance energy resiliency for coastal communities by developing an integrated renewable energy test platform that will model how multiple renewable energy resources could power shoreline businesses or communities (Figure 1). The test bed will demonstrate how different renewable systems can be integrated to reduce carbon emissions and contribute to a net zero emissions site and provide lessons, controls, and protocols that will help to expand energy options for shoreline and maritime businesses. The integrated system is expected to serve as a test bed for shoreline power and ocean energy technologies for years to come.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗