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At least 1,585 records · Page 88
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Radiological risk of building materials using homemade airtight radon chamber

Soil based building materials known to contain various amounts of natural radionuclide mainly {sup 238}U and {sup 232}Th series and {sup 40}K. In general most individuals spend 80% of their time indoors and the natural radioactivity in building materials is a main source of indoor radiation exposure. The internal exposure due to building materials in dwellings and workplaces is mainly caused by the activity concentrations of short lived {sup 222}Radon and its progenies which arise from the decay of {sup 226}Ra. In this study, the indoor radon concentration emanating from cement brick, red-clay brick, gravel aggregate and Portland cement samples were measured in a homemade airtight radon chamber using continuous radon monitor 1029 model of Sun Nuclear. Radon monitor were left in the chamber for 96 hours with an hour counting time interval. From the result, the indoor radon concentrations for cement brick, red-clay brick, gravel aggregate and Portland cement samples determined were 396 Bq m{sup −3}, 192 Bq m{sup −3}, 176 Bq m{sup −3} and 28 Bq m{sup −3}, respectively. The result indicates that the radon concentration in the studied building materials have more than 100 Bq m{sup −3} i.e. higher than the WHO action level except for Portland cement sample. The calculated annual effective dose for cement brick, red-clay brick, gravel aggregate and Portland cement samples were determined to be 10 mSv y{sup −1}, 4.85 mSv y{sup −1}, 4.44 mSv y{sup −1} and 0.72 mSv y{sup −1}, respectively. This study showed that all the calculated effective doses generated from indoor radon to dwellers or workers were in the range of limit recommended ICRP action levels i.e. 3 - 10 mSv y{sup −1}. As consequences, the radiological risk for the dwellers in terms of fatal lifetime cancer risk per million for cement brick, red-clay brick, gravel aggregate and Portland cement were calculated to be 550, 267, 244 and 40 persons respectively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Earthquake response of storey building in Jakarta using accelerographs data analysis

As seismotectonic, the Jakarta city will be greatly affected by the earthquake which originated from the subduction zone of the Sunda Strait and south of Java. Some occurrences of earthquakes in these location are often perceived by the occupants in the upper floors of multi-storey buildings in Jakarta but was not perceived by the occupants on the ground floor. The case shows the difference in ground-motion parameters on each floor height. The analysis of the earthquake data recorded by accelerographs on different floors need to be done to know the differences in ground-motion parameters. Data used in this research is accelerograph data installed on several floors in the main building of Meteorology Climatology and Geophysics Agency with a case study of Kebumen earthquake on January 25{sup th} 2014. Parameters analyzed include the Peak Ground Acceleration (PGA), Peak Ground Displacement (PGD), Peak Spectral Acceleration (PSA), Amplification (Ag), and the Effective Duration of earthquake (t{sub e}). Research stages include accelerographs data acquisition in three (3) different floors, conversion and data partition for each component, conversion to units of acceleration, determination of PGA, PGD, PSA, Ag and t{sub e} as well as data analysis. The study shows the value of PGA on the ground floor, 7{sup th} floor and 15{sup th} floors, respectively are 0.016 g, 0.053 g and 0.116 g. PGD on the ground floor, 7{sup th} floor and 15{sup th} floor respectively are 2.15 cm, 2.98 cm and 4.92 cm. PSA on the ground floor, 7{sup th} floor and 15{sup th} floor respectively are 0.067 g, 0.308 g and 0.836 g. Amplification of the peak acceleration value on the ground floor, 7{sup th} floor and 15{sup th} floor to the surface rock are 4.37, 6.07 and 7.30. Effective duration of the earthquake on the ground floor, 7{sup th} floor and 15{sup th} floor respectively are 222.28 s, 202.28 s and 91.58 s. In general, with increasing floor of the building, the value of the peak ground acceleration, peak ground displacement, peak spectral acceleration and amplification growing, contrary to the value of the effective duration of earthquake decreases. The difference in this parameter is strongly influenced by local soil conditions and building construction.

58 GEOSCIENCES↗
DOE OSTI2017

Global scenarios of urban density and its impacts on building energy use through 2050

Significance Urban density significantly impacts urban energy use and the quality of life of urban residents. Here, we provide a global-scale analysis of future urban densities and associated energy use in the built environment under different urbanization scenarios. The relative importance of urban density and energy-efficient technologies varies geographically. In developing regions, urban density tends to be the more critical factor in building energy use. Large-scale retrofitting of building stock later rather than sooner results in more energy savings by the middle of the century. Reducing building energy use, improving the local environment, and mitigating climate change can be achieved through systemic efforts that take potential co-benefits and trade-offs of both higher urban density and building energy efficiency into account.

42 ENGINEERING↗
DOE OSTI2022

A high-fidelity building performance simulation test bed for the development and evaluation of advanced controls

We present an open-source building performance simulation test bed, the Advanced Controls Test Bed (ACTB), that interfaces high-fidelity Spawn of EnergyPlus building models, with advanced controllers implemented in Python. Additionally, the ACTB leverages the Building Optimization Testing and Alfalfa platforms for managing simulations, providing an external clock, a representational state transfer (REST) application programming interface (API), and key performance indicators for evaluating the effectiveness of control strategies. The REST API allows the development of external controllers programmed in languages such as Python, which provides flexibility and a rich choice of scientific libraries for designing control sequences. We present three test cases based on the U.S. Department of Energy's Reference Small Office Building to demonstrate the ACTB's capabilities: (a) rule-based controls compliant with ASHRAE Guideline 36 control sequences; (b) an economic model predictive control implemented using do-mpc; and (c) a deep Q-network reinforcement learning agent implemented using OpenAI Gym.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Evaluation of the Radioactive Material Released in the Harborview Research and Training Building and Some Implications for Emergency Response

On 2 May 2019, during the 137 Cs source recovery operation, a source capsule in a research irradiator containing approximately 77.1 TBq was breached. Based on a geometric reconstruction analysis of the damage to the capsule, approximately 46.3 GBq (0.04%) was impacted by the chop saw (grinder) inside a mobile hot cell on the loading dock at the University of Washington Harborview Research and Training (HRT) Building. A very small fraction of the material impacted, less than 1%, was released from the mobile hot cell and then to the rest of the HRT Building. The objectives of this project were to assess the accidental release of 137 CsCl and its implications related to emergency response methods and the ramifications of 137 CsCl transport. The phenomenology of this event was also compared with past alkali halide dispersal events. The vast number of measurements and samples collected by the remediation contractors, the Department of Energy’s Nuclear Emergency Support Team, and the small number of retrospective samples collected by the authors informed the analysis. The techniques included (1) autoradiography and electron microscopy of samples collected from the HRT Building and the irradiator, (2) 3D visualization of deposition on surfaces and within the ventilation system, and (3) a study of the damage to the source capsule to evaluate the Cs particle size and particle composition due to the grinding accident. Subsequently, the cesium contaminant transport through the numerous pathways in the building was reconstructed to assess the deposition on surfaces as a function of particle size. Furthermore, the implications for emergency response are relevant to data quality and management. A Data Quality Objective guides data collection methods so that they have appropriate accuracy and precision for the intended application. Recommendations were made with respect to the sample collection protocols and archiving of samples.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2018

Optimal Energy Consumption Forecast for Grid Responsive Buildings: A Sensitivity Analysis

It is envisioned that building systems will become active participants in the smart grid operation by controlling their energy consumption to optimize complex criteria beyond ensuring local end-use comfort satisfaction. A forecast of the building energy consumption will be necessary to enable integration between building and grid operation. Such forecast will be affected by parametric and measurement uncertainty. In this paper we develop a methodology for quantifying the sensitivity of optimal hourly energy consumption forecasts to various sources model and measurement uncertainty. We demonstrate the approach for a building heating ventilation and air conditioning (HVAC) system use-case.

Kundu, Soumya↗
DOE OSTI2024

Efficient Extraction Of Building Elevation Attributes For Flood Risk Management Using Airborne LiDAR Data

In this paper, we address the need for extracting two key building elevation attributes—Lowest Adjacent Grade (LAG) and Highest Adjacent Grade (HAG)—which are crucial for effective flood risk management. Conventional methods, involving onsite surveying or the use of optical imagery-derived building footprints combined with Digital Elevation Models (DEMs), often face misalignment and time discrepancy issues due to varied remote sensing sources. We introduce a new, scalable method that exclusively relies on airborne LiDAR data to overcome these challenges. Our approach employs an object-based ground filtering technique, and the results were evaluated using two different DEMs and building footprint sets. The findings demonstrate that our single-source method, utilizing only airborne LiDAR data, significantly improves the accuracy of LAG and HAG calculations compared to traditional methods that use hand-digitized building footprints. The proposed approach offers a solution for comprehensive flood risk management endeavors.

Song, Hunsoo↗
DOE OSTI2023

Convergence Rate of Model Reference Adaptive Control with Application to Building HVAC Systems

Model reference adaptive control (MRAC) has been studied for decades and successfully applied in multiple areas, including heating, ventilation, and air conditioning (HVAC) systems for buildings. MRAC is efficient in capturing the time-varying characteristics of buildings' indoor temperatures and outdoor weather environments. In this paper, the rate of convergence of MRAC is investigated, where a direct adaptive control with temperature set point reference tracking is used to regulate the indoor temperatures for buildings. Numerical results show that by controlling the HVAC systems of residential buildings using MRAC, the indoor temperatures converge Q-sublinearly to the desired temperature set points. In addition, the rate of convergence for MRAC is compared with a baseline adaptive model-free control method.

Wu, Tumin↗
DOE OSTI2019

Community Microgrid Planning Considering Building Thermal Dynamics

In this paper, the building thermal dynamic characteristics are introduced in the community microgrid (MG) planning model. The proposed planning model is formulated as a mixed integer linear programming (MILP) which seeks to determine the optimal deployment strategy for various distributed energy resources (DER). The objective is to minimize the annualized cost of community MG, including investment cost in DER, operation cost for dispatchable fuel-generators (DFG), energy storage system (ESS) degradation cost, energy purchasing and peak demand charge at PCC, customer discomfort cost due to the room temperature deviation and load curtailment cost. Given the slow thermal dynamic characteristics of buildings, the heating, ventilation and air-conditioning (HVAC) system in the proposed model is treated as a demand side management (DSM) component, whose dispatch commands are provided by the central MG controller. Numerical results based on a community MG comprising of 20 residential buildings demonstrate the effectiveness of the proposed planning model and the benefits of introducing the building thermal dynamic model.

Zhang, Xiaohu↗
DOE OSTI2013

Simulation and big data challenges in tuning building energy models

EnergyPlus is the flagship building energy simulation software used to model whole building energy consumption for residential and commercial establishments. A typical input to the program often has hundreds, sometimes thousands of parameters which are typically tweaked by a buildings expert to “get it right”. This process can sometimes take months. “Autotune” is an ongoing research effort employing machine learning techniques to automate the tuning of the input parameters for an EnergyPlus input description of a building. Even with automation, the computational challenge faced to run the tuning simulation ensemble is daunting and requires the use of supercomputers to make it tractable in time. In this paper, we describe the scope of the problem, particularly the technical challenges faced and overcome, and the software infrastructure developed/in development when taking the EnergyPlus engine, which was primarily designed to run on desktops, and scaling it to run on shared memory supercomputers (Nautilus) and distributed memory supercomputers (Frost and Titan). The parametric simulations produce data in the order of tens to a couple of hundred terabytes. We describe the approaches employed to streamline and reduce bottlenecks in the workflow for this data, which is subsequently being made available for the tuning effort as well as made available publicly for open-science.

Sanyal, Jibonananda↗
DOE OSTI2018

Online Verification of Transactive Control for Commercial Buildings

Commercial buildings account for a significant portion of electrical energy demand in the United States. Thus, they serve as an ideal target for energy efficiency improvements, which can reduce congestion on the electrical grid and result in lower payments for building owners. One of the proposed approaches to improving energy efficiency in commercial buildings is transactive control, which allows system components to negotiate for resources within a market. The market is designed so that the control objective is met when components consume their allocation of energy. In this paper, a method is proposed to verify that the consumption of market participants matches their allocation. The method regards energy allocations at the beginning of market periods as predictions. Using collected measurements, actual consumption is then estimated at the end of the market period. Agreement between the predicted and estimated use indicates that the system component is behaving as expected. Initial results from the deployment of this verification method in a commercial building are reported, demonstrating the practicality and utility of the method.

Follum, James D.↗
DOE OSTI2024

Optimizing Grid-interactive Efficient Building Designs with Stacked Value Streams

Grid-interactive efficient buildings (GEBs) are those characterized by the combination of energy efficiency and demand flexibility with smart technologies and communications to not only deliver greater affordability and comfort to buildings, but also help utilities manage grid operations and lower system costs. This paper presents an innovative techno-economic assessment framework to effectively examine different GEB design options, explore various use cases, define technically achievable benefits, and thereby assist in informed decision-making. In particular, building load flexibility, thermal storage, and battery energy storage are considered. Advanced optimal dispatch problem is formulated to maximize the stacked value streams from multiple, competing use cases, subject to the physical capabilities and operational flexibility associated with different designs and configurations. Comprehensive case studies were performed for a real-world building to evaluate the cost-effectiveness of different GEB designs and offer in-depth insights. It was found that the proposed assessment method could effectively capture the costs and benefits linked to each GEB design option. Furthermore, the study revealed that outage mitigation and demand response are the two most significant sources of benefits for GEBs.

Ma, Xu↗
DOE OSTI2017

A Testbed Environment for Buildings-to-Grid Cyber Resilience Research and Development

The Smart Grid is characterized by the proliferation of advanced digital controllers at all levels of its operational hierarchy from generation to end consumption. Such controllers within modern residential and commercial buildings enable grid operators to exercise fine-grained control over energy consumption through several emerging Buildings-to-Grid (B2G) applications. Though this capability promises significant benefits in terms of operational economics and improved reliability, cybersecurity weaknesses in the supporting infrastructure could be exploited to cause a detrimental effect and this necessitates focused research efforts on two fronts. First, the understanding of how cyber attacks in the B2G space could impact grid reliability and to what extent. Second, the development and validation of cyber-physical application-specific countermeasures that are complementary to traditional infrastructure cybersecurity mechanisms for enhanced cyber attack detection and mitigation. The PNNL B2G testbed is currently being developed to address these core research needs. Specifically, the B2G testbed combines high-fidelity buildings+grid simulators, industry-grade building automation and Supervisory Control and Data Acquisition (SCADA) systems in an integrated, realistic, and reconfigurable environment capable of supporting attack-impact-detection-mitigation experimentation. In this paper, we articulate the need for research testbeds to model various B2G applications broadly by looking at the end-to-end operational hierarchy of the Smart Grid. Finally, the paper not only describes the architecture of the B2G testbed in detail, but also addresses the broad spectrum of B2G resilience research it is capable of supporting based on the smart grid operational hierarchy identified earlier.

Sridhar, Siddharth↗
DOE OSTI2020

Building Large-Scale U.S. Synthetic Electric Distribution System Models

Rapid increases in distributed energy resources on distribution systems are prompting research efforts to improve and evaluate electric power distribution algorithms; however, there is a shortage of realistic, large-scale, U.S.-style test systems for the evaluation of such advanced algorithms. Some available tools to build large-scale test systems are of European style, and their application to studies focused on the United States might not be desirable given very different characteristics between the European and U.S. distribution designs. Motivated by this need, this paper develops detailed algorithms to build large-scale U.S. distribution systems and incorporates them in a new Reference Network Model, RNM-US. The approach starts with information from street maps and a catalog with electric equipment that includes power lines, transformers, voltage regulators, capacitors, and switching devices. The paper presents the algorithms through an illustrative case study of the different steps that comprise the process of building a synthetic distribution grid. Finally, the paper presents a medium- and a large-scale data set covering 10 million electrical nodes and 120,000 km of power lines, demonstrating the applicability of the proposed method to build very large-scale synthetic distribution systems.

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2019

Coordination of Behind-the-Meter Energy Storage and Building Loads: Optimization with Deep Learning Models*

With the increasing penetration of renewable energy systems and energy storage systems in buildings, it is critical to optimize system operation. In this paper, we develop an optimization strategy to minimize the operation cost as well as maintain indoor thermal comfort, for a building integrated with battery and PV. A Recurrent Neural Network (RNN) is used to predict building thermal load and zone temperatures. A black-box optimization algorithm known as nonlinear optimization by mesh adaptive direct search (NOMADS) is employed in simulation to provide look-ahead optimal battery dispatch and zone temperature set-point schedules so that the operation cost is minimized. Field data collected from a medium sized office building at Pacific Northwest National Laboratory (PNNL) integrated with Photovoltaic (PV) system model and battery storage system model is used to demonstrate the proposed methodology. Compared with rule based methods, the optimization strategy obtained a lower cost of operation while satisfying comfort constraints.

Chen, Yimin↗
DOE OSTI2024

Multivariate Testing of Sampling Techniques to Address Class Imbalance in Building Use Type Classification

This study addresses the challenges inherent in building use type classification, particularly focusing on the issue of class imbalance in the training datasets for machine learning classifiers. We comprehensively analyze the efficacy of various class-balancing sampling techniques. Employing Monte Carlo simulations and Bayesian optimization, we evaluated the performance of multiple sampling methods, including Random Oversampling, Random Undersampling, SMOTE, Borderline-SMOTE, and ADASYN, across a dataset encompassing nine southeastern coastal states of the United States. Our findings reveal that simple random over- and undersampling techniques outperform more sophisticated methods. Additionally, we show inherent value in creating an imbalance in training data to effectively train a machine learning classifier for distinguishing between residential and nonresidential buildings. This study provides valuable guidance for future research on building use type classification research and lays essential groundwork for developing attribute-rich building stock datasets.

Adams, Daniel↗
DOE OSTI2025

I Can’t Read All That! Improving the Usability of Semantic Models Using Concise, Ontology-Agnostic, Building-Specific Schemas

Semantic ontologies have enabled the creation of formalized, machine-readable descriptions of heterogenous building systems by providing dictionaries of well defined concepts that can be applied to model them. Within a semantic model of a particular building, a subset of an ontology's concepts may be applied in different ways to represent a particular perspective of the building's systems. How the concepts were applied can only be understood by examining the large amount of instance data within a semantic model, which leads to usability challenges. We propose a concise, ontology-agnostic method for defining building-specific schema (b-schema) graphs that summarize the structure and content of a semantic model. This approach provides a queryable and concise representation of the model's contents, separate from the instance data within a model, that can mitigate the challenges posed by the size and complexity of semantic models in processes such as visualization, querying, validation, and the use of large language models (LLMs). We validate our approach on semantic models based on the Brick and ASHRAE S223 ontologies. Results demonstrate that b-schemas significantly reduce the complexity of visual interpretation, accelerate SPARQL queries and SHACL validation, and improve LLM-based knowledge graph question answering.

Paul, Lazlo [Lawrence Berkeley National Laboratory↗
DOE OSTI2018

BESTEST-GSR (Building Energy Simulation Test - Generation Simulation and Reporting) [SWR 18-23]

Building Energy Simulation Test (BESTEST) is an NREL-developed method to validate the qualitative performance of different whole building simulations engines relative to each other. https://www.nrel.gov/docs/legosti/old/6231.pdf For EnergyPlus® and OpenStudio®, generation of test cases, simulation, and reporting has been a manual or partially automated process that typically took a number of months after a new version of EnergyPlus came out. Building Energy Simulation Test - Generation Simulation and Reporting (BESTEST-GSR) was developed to fully automate this process so it is less error-prone, less time-consuming, and can be quickly run on request for any development branch of EnergyPlus and OpenStudio. The initial use case is to directly support OpenStudio and EnergyPlus development, but the software is configured to support use by external software developers that build tools on top of EnergyPlus and/or OpenStudio. Tools built on top of OpenStudio can leverage the test generation, simulation, and reporting, while tools built on EnergyPlus can leverage the test simulation and reporting. The model generation is accomplished by running OpenStudio measures for envelope, heating, or cooling test cases. At this stage the specified weather file required for a specific test case is assigned to the model. Simulation is enabled using the OpenStudio Command Line Interface (CLI) to run a series of OpenStudio and Reporting measures to a specified seed model. The specified workflow is defined with an OpenStudio OSW file. The first measure generates the test case model, while the second measure is a reporting measure that runs after the simulation to collect necessary simulation results from the EnergyPlus SQL file. The Reporting stage gathers data from individual OpenStudio OSW files and populates a series of BESTEST Microsoft Excel spreadsheets. BESTEST-GSR also gathers zip files from individual simulations for diagnostic purposes.

Goldwasser, David↗