SEARCH · Engineering Papers
Results for “building simulation”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Building a Simulation Community for Solving Exascale Problems.
Abstract not provided.
Building a Simulation Community for Solving Exascale Problems: The Structural Simulation Toolkit (SST).
Abstract not provided.
CAD-Integrated Web-Based Building Performance Simulation Platform
Explore the source record for details and available documents.
Modeling the Interaction Between Energy Efficiency and Demand Response on Regional Grid Scales: Preprint
With increasing penetration of intermittent renewable generation at grid and distributed scales, flexible building loads can provide significant system value and support the evolving needs of the grid. The growing value of load flexibility may complicate the traditional separation between energy efficiency (EE) and demand response (DR). EE measures may compete in some cases with a building’s DR capabilities but complement one another in other cases. EE can also increase or decrease the need for DR at the system level and change the availability of DR to meet system needs. In this study we present a bottom-up approach to modeling interactive effects between EE and DR in buildings within two regions of the US electricity grid. From a library of building simulation models for different buildings and climates, we synthesize system-level demand profiles and the impacts of potential future EE portfolios. Coupling the underlying building models with a database of DR-enabling technologies, we then compute the quantity of DR that can be delivered in each scenario. The results show that EE and DR interactions are largely driven by the timing of EE savings that are measure-specific and the coincidence with system peak demand that is region-specific. We also find that perspective of the impacts matters – for instance that some EE measures reduce the system need for DR but also reduce the DR potential. Our results imply that utility EE and DR programs developed without considering interactive effects may lead to increased grid-management challenges over the long term.
CAD-Integrated Web-Based Building Performance Simulation Platform
Explore the source record for details and available documents.
Evaluation of Existing Infiltration Models Used in Building Energy Simulation
Explore the source record for details and available documents.
CAD-Integrated Web-Based Building Performance Simulation Platform
Explore the source record for details and available documents.
EnergyPlus Building Energy Simulations of High-Performance Vapor-Compression Based Cold Climate Water Heaters
Explore the source record for details and available documents.
Equation-based languages – A new paradigm for building energy modeling, simulation and optimization
Most of the state-of-the-art building simulation programs implement models in imperative programming languages. This complicates modeling and excludes the use of certain efficient methods for simulation and optimization. In contrast, equation-based modeling languages declare relations among variables, thereby allowing the use of computer algebra to enable much simpler schematic modeling and to generate efficient code for simulation and optimization. We contrast the two approaches in this paper. We explain how such manipulations support new use cases. In the first of two examples, we couple models of the electrical grid, multiple buildings, HVAC systems and controllers to test a controller that adjusts building room temperatures and PV inverter reactive power to maintain power quality. In the second example, we contrast the computing time for solving an optimal control problem for a room-level model predictive controller with and without symbolic manipulations. As a result, exploiting the equation-based language led to 2, 200 times faster solution
An Interactive Visualization Tool for Large-Scale Building Stock Modeling
Recent advancements in data science and high-performance computing are making it easier to run millions of building simulations, but meaningful visualization of such large datasets remains a challenge. This paper presents a new tool developed to view the results of large-scale OpenStudio simulations of national, regional, or local building stocks. The tool processes millions of simulations to calculate measure savings, utility bills, carbon emissions, primary energy, and cost-effectiveness metrics at a high geographic resolution. Interactive visualizations of the building characteristics, consumption, and measure savings data include proportional symbol maps and histogram plots and can be filtered by any building characteristic.
An Interactive Visualization Tool for Large-Scale Building Stock Modeling: Preprint
Recent advancements in data science and high-performance computing are making it easier to run millions of building simulations, but meaningful visualization of such large datasets remains a challenge. This paper presents a new tool developed to view the results of large-scale OpenStudio simulations of national, regional, or local building stocks. The tool processes millions of simulations to calculate measure savings, utility bills, carbon emissions, primary energy, and cost-effectiveness metrics at a high geographic resolution. Interactive visualizations of the building characteristics, consumption, and measure savings data include proportional symbol maps and histogram plots and can be filtered by any building characteristic.
Multi-Variable Parametric Analysis of Prototype Building Energy Performance Using Current and Future Weather Scenarios For Data-Driven Market Transformation Support
This project aimed to develop a public building simulation data set that may be used to inform building code development and guidelines for building innovation. The data set consists of several common building types and many representative locations across the United States. A parametric design of building properties was developed to create a range of building energy models that represent common building design decisions with a particular focus on fenestration options. The US Department of Energy prototype building energy models were altered according to a parametric building design and simulated using both current weather data and future weather estimates derived from global climate models. The resulting data set allows for pertinent exploration of building design parameters, including fenestration, within different environments across the United States in the broader context of climate change.
A High-Granularity Approach to Modeling Energy Consumption and Savings Potential in the U.S. Residential Building Stock
Building simulations are increasingly used in various applications related to energy efficient buildings. For individual buildings, applications include: design of new buildings, prediction of retrofit savings, ratings, performance path code compliance and qualification for incentives. Beyond individual building applications, larger scale applications (across the stock of buildings at various scales: national, regional and state) include: codes and standards development, utility program design, regional/state planning, and technology assessments. For these sorts of applications, a set of representative buildings are typically simulated to predict performance of the entire population of buildings. Focusing on the U.S. single-family residential building stock, this paper will describe how multiple data sources for building characteristics are combined into a highly-granular database that preserves the important interdependencies of the characteristics. We will present the sampling technique used to generate a representative set of thousands (up to hundreds of thousands) of building models. We will also present results of detailed calibrations against building stock consumption data.
A High-Granularity Approach to Modeling Energy Consumption and Savings Potential in the U.S. Residential Building Stock: Preprint
Building simulations are increasingly used in various applications related to energy efficient buildings. For individual buildings, applications include: design of new buildings, prediction of retrofit savings, ratings, performance path code compliance and qualification for incentives. Beyond individual building applications, larger scale applications (across the stock of buildings at various scales: national, regional and state) include: codes and standards development, utility program design, regional/state planning, and technology assessments. For these sorts of applications, a set of representative buildings are typically simulated to predict performance of the entire population of buildings. Focusing on the U.S. single-family residential building stock, this paper will describe how multiple data sources for building characteristics are combined into a highly-granular database that preserves the important interdependencies of the characteristics. We will present the sampling technique used to generate a representative set of thousands (up to hundreds of thousands) of building models. We will also present results of detailed calibrations against building stock consumption data.
Using the BEopt Automated Residential Simulation Test Suite to Enable Comparative Analysis Between Energy Simulation Engines: Preprint
Verification and validation are crucial software quality control procedures when developing and implementing models. This is particularly important as a variety of stakeholders rely on accurate predictions from building simulation programs. This study uses the BEopt Automated Residential Simulation Test Suite (BARTS) to facilitate comparison of two energy simulation engines across various building components and includes models that isolate the impacts of specific building components on annual energy consumption. As a case study, BARTS has been used to identify important discrepancies between the engines for several components of the building models; these discrepancies are caused by differences in the models used by the engines or coding errors.
Utility-scale Building Type Assignment Using Smart Meter Data
United States building energy use accounted for 40% of total energy use, 74% of peak demand, and $412 billion in 2019. Building energy modeling allows researchers to simulate building physics, gain insights into possible energy/demand saving opportunities, and assess cost-effective resilience amidst climate change. Many building features needed to create building energy models are readily available such as 2D footprints and LiDAR (height). A critical feature that is not generally obtainable is the building type. In partnership with a utility, a years worth of real-world, 15-minute electrical use data has been examined. The smart meter data is compared to 97 different prototype building energy models to assign building type. Real-world considerations including data preparation, quality assurance, and handling of missing values for advanced metering infrastructure data are addressed. Euclidean distance for pattern-matching of energy use, dynamic time warping, and time-window statistics with machine learning are compared for determining building type from measured electricity use.
Development of a hardware-in-the-loop testbed for laboratory performance verification of flexible building equipment in typical commercial buildings
The goals of reducing energy costs, shifting electricity peaks, increasing the use of renewable energy, and enhancing the stability of the electric grid can be met in part by fully exploiting the energy flexibility potential of buildings and building equipment. The development of strategies that exploit these flexibilities could be facilitated by publicly available high-resolution datasets illustrating how control of HVAC systems in commercial buildings can be used in different climate zones to shape the energy use profile of a building for grid needs. This article presents the development and integration of a Hardware-In-the-Loop Flexible load Testbed (HILFT) that integrates physical HVAC systems with a simulated building model and simulated occupants with the goal of generating datasets to verify load flexibility of typical commercial buildings. Compared to simulation-only experiments, the hardware-in-the-loop approach captures the dynamics of the physical systems while also allowing efficient testing of various boundary conditions. The HILFT integration in this article is achieved through the co-simulation among various software environments including LabVIEW, MATLAB, and EnergyPlus. Although theoretically viable, such integration has encountered many real-world challenges, such as: 1) how to design the overall data infrastructure to ensure effective, robust, and efficient integration; 2) how to avoid closed-loop hunting between simulated and emulated variables; 3) how to quantify system response times and minimize system delays; and 4) how to assess the overall integration quality. Lessons-learned using the examples of an AHU-VAV system, an air-source heat pump system, and a water-source heat pump system are presented.