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The commercial building sector stock model, or ComStock, is a highly granular, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual subhourly energy consumption of the commercial building stock across the United States.
The performance of model predictive control (MPC) can be significantly affected by different choices of controller parameters such as the time intervals for model discretization and control sampling. Due to the lack of a systematic understanding on how these parameters affect control performance, they are usually selected arbitrarily in practice.In this paper, the combined impacts of selected time intervals for model discretization and control sampling on the performance of MPC are comprehensively investigated for the first time through detailed simulations. Specifically, a typical MPC strategy is first designed to improve building operations based on a reduced-order model of building dynamics. Then, the performance of the designed MPC is evaluated against different choices of time intervals for model discretization and control sampling on a simulated office building. The detailed simulation results reveal that the time interval for model discretization has a much greater influence on the performance of MPC than the time interval for control sampling. Although the time interval for control sampling usually receives more attentions in practice, it turns out that the time interval for model discretization affects the prediction performance, cost saving, and computation time simultaneously and more significantly. Therefore, the simulation-based performance evaluation presented here sheds light on the impacts of different time intervals and facilitates their selection for practical applications of MPC to building operations
The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources. To overcome this challenge, new ideas on surrogate models using machine learning methods are being explored to replace computationally expensive components. Additionally, differentiable programming has been proposed as a complementary approach, providing controllable and scalable simulation routines. In this document, new and ongoing efforts for surrogate models and differential programming applied to detector simulation are discussed in the context of the 2021 Particle Physics Community Planning Exercise (`Snowmass').
Vertical-axis wind turbines (VAWTs) have a long history, with a wide variety of turbine archetypes that have been designed and tested since the 1970s. While few utility-scale VAWTs currently exist, the placement of the generator near the turbine base could make VAWTs advantageous over tradition horizontal-axis wind turbines for floating offshore wind applications via reduced platform costs and improved scaling potential. However, there are currently few numerical design and analysis tools available for VAWTs. One existing engineering toolset for aero-hydro-servo-elastic simulation of VAWTs is the Offshore Wind ENergy Simulator (OWENS), but its current modeling capability for floating systems is non-standard and not ideal. This article describes how OWENS has been coupled to several OpenFAST modules to update and improve modeling of floating offshore VAWTs and discusses the verification of these new capabilities and features. The results of the coupled OWENS verification test agree well with a parallel OpenFAST simulation, validating the new modeling and simulation capabilities in OWENS for floating VAWT applications. These developments will enable the design and optimization of floating offshore VAWTs in the future.
Building on the successfully completed effort to calibrate and validate the U.S. Department of Energy's ResStock TM and ComStock TM models over the past three years, the objective of this work is to produce national data sets that empower analysts working for federal, state, utility, city, and manufacturer stakeholders to answer a broad range of analysis questions. The goal of this work is to develop energy efficiency, electrification, and demand flexibility end-use load shapes (electricity, gas, propane, or fuel oil) that cover a majority of the high-impact, market-ready (or nearly market-ready) measures. "Measures" refers to energy efficiency variables that can be applied to buildings during modeling. An end-use savings shape is the difference in energy consumption between a baseline building and a building with an energy efficiency, electrification, or demand flexibility measure applied. It results in a time-series profile that is broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each timestep. ComStock is a highly granular, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual sub hourly energy consumption of the commercial building stock across the United States. The baseline model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology and results of the baseline model are discussed in the final technical report of the End-Use Load Profiles project. This documentation focuses on a single end-use savings shape measure—boiler replacement by air-source heat pump boiler. This measure replaces space heating natural gas boilers by air-source heat pump boilers when applicable and helps quantify the decarbonization as well as the energy savings potential from the replacement. The measure resulted higher savings in natural gas consumption compared to the increase in electricity consumption, with a ratio of 2.9. The total natural gas energy consumption was reduced by 20%, whereas the total electricity consumption was increased by 2.5%.
This report assesses the potential for nationwide adoption of a duct static pressure reset in MZ VAV systems in appropriate applications. Building on the 3-year End-Use Load Profiles project to calibrate and validate the U.S. Department of Energy’s ResStock™ and ComStock™ models, this work produces national datasets that enable cities, states, utilities, and other stakeholders to answer a broad range of questions regarding their commercial building stock. ComStock is a highly granular, bottom-up model that uses various data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual sub-hourly energy consumption of the commercial building stock across the United States. The “baseline” model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology of the baseline model is discussed in the ComStock Reference Documentation. The goal of this work is to develop energy efficiency and demand flexibility measures that cover market-ready technologies and study their mass-adoption impact on the baseline building stock. “Measures” refers to various “what-if” scenarios that can be applied to buildings. The results for the baseline and measure scenario simulations are published in public datasets that provide insights into building stock characteristics, operational behaviors, utility bill impacts, and annual and sub-hourly energy usage by fuel type and end use. This report describes the modeling methodology for a single ComStock measure scenario—Fan Static Pressure Reset for Multizone Variable Air Volume (VAV) Systems—and briefly introduces key results. The full public dataset can be accessed on the ComStock data lake or via the Data Viewer at comstock.nrel.gov. The public dataset enables users to create custom aggregations of results for their use case (e.g., filter to a specific county or building type).
This report assesses the potential for nationwide adoption of thermostat setbacks in appropriate applications. Building on the 3-year End-Use Load Profiles project to calibrate and validate the U.S. Department of Energy’s ResStock™ and ComStock™ models, this work produces national datasets that enable cities, states, utilities, and other stakeholders to answer a broad range of questions regarding their commercial building stock. ComStock is a highly granular, bottom-up model that uses various data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual sub-hourly energy consumption of the commercial building stock across the United States. The “baseline” model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology of the baseline model is discussed in the ComStock Reference Documentation. The goal of this work is to develop energy efficiency and demand flexibility measures that cover market-ready technologies and study their mass-adoption impact on the baseline building stock. “Measures” refers to various “what-if” scenarios that can be applied to buildings. The results for the baseline and measure scenario simulations are published in public datasets that provide insights into building stock characteristics, operational behaviors, utility bill impacts, and annual and sub-hourly energy usage by fuel type and end use. This report describes the modeling methodology for a single ComStock measure scenario— Thermostat Setbacks During Unoccupied Periods—and briefly introduces key results. The full public dataset can be accessed on the ComStock data lake or via the Data Viewer at comstock.nrel.gov. The public dataset enables users to create custom aggregations of results for their use cases (e.g., filter to a specific county or building type).
This article summarizes the analysis conducted by Pacific Northwest National Laboratory (PNNL), assessing expected end-use energy consumption in commercial buildings, based on recent editions of the model energy code for the commercial sector, ANSI/ASHRAE/IES Standard 90.1, Energy Standard for Buildings Except Low-Rise Residential Buildings. The results represent simulated energy use based on) Commercial Prototype Building Models1 across representative climate zones in the United States, as defined by Standard 90.1. PNNL examined the resulting simulation outputs to assess how energy is used across primary systems within prominent U.S. commercial building types to understand how energy is used in each building type at the end-use level and to identify areas for improvements in future code cycles.
Spawn is the latest whole-building energy simulation engine developed by the US Department of Energy, National Labs and industry. Whereas EnergyPlus was designed as a successor to DOE-2, Spawn is not a direct successor of–nor is it intended as an imminent replacement for– EnergyPlus. Instead, Spawn reuses parts of EnergyPlus while supporting new use cases in HVAC and controls. Spawn is intended to provide several capabilities that significantly advance beyond EnergyPlus. It is intended to support the evaluation of novel HVAC and district energy systems in a more physically realistic way. Critically, it can model control in a physically realistic way, using portable specifications that can be compiled for execution on control platforms. Spawn is also intended to support co-simulation in an intrinsic way to enable integration with third-party models. This paper describes the software architecture of Spawn from model authoring to compilation and simulation. It explains how Spawn reuses the envelope and daylighting modules of EnergyPlus and couples them to HVAC and control models from the Modelica Buildings Library using the Functional Mockup Interface (FMI) standard. It presents a number of examples that: i) validate Spawn’s coupled simulation approach by comparing its results to those of EnergyPlus, ii) illustrate the Spawn methodology for modeling and simulating HVAC systems, and iii) evaluate the performance of Spawn’s Quantized State System (QSS) time integration algorithms
To simulate the airflow through a wind farm across a wide range of atmospheric conditions, microscale models (e.g., large-eddy simulation, LES, models) have to be coupled with mesoscale models, because microscale models lack the atmospheric physical processes to represent time-varying local forcing. Here we couple mesoscale model outputs to a LES solver by applying mesoscale momentum- and temperature-budget components from the Weather Research and Forecasting model to the governing equations of the Simulator fOr Wind Farm Applications model. We test whether averaging the budget components affects the LES results with regard to quantities of interest to wind energy. Our study focuses on flat terrain during a quiescent diurnal cycle. The simulation results are compared with observations from a 200-m tall meteorological tower and a wind-profiling radar, by analyzing time series, profiles, rotor-averaged quantities, and spectra. However, while results show that averaging reduces the spatio-temporal variability of the mesoscale momentum-budget components, when coupled with the LES model, the mesoscale bias (in comparison with observations of wind speed and direction, and potential temperature) is not reduced. In contrast, the LES technique can correct for shear and veer. In both cases, however, averaging the budget components shows no significant impact on the mean flow quantities in the microscale and is not necessary when coupling mesocale budget components to the LES model.
Calibrated building energy simulation is an important pathway to more energy-efficient buildings, but the information requirements of some approaches to this problem are significant. This is particularly true for supermarkets and other so-called “big-box” retail stores. Another characteristic of supermarkets is the significant interaction between Heating Ventilating and Air Conditioning (HVAC) and refrigeration systems in these buildings. These buildings could contain a wide variety of systems and a degree of load diversity that makes calibrated modeling a challenge. This paper describes a simplified approach that uses OpenStudio and EnergyPlus to combine known building parameters with “typical” parameters, resulting in a simplified building that is amenable to calibration. This approach was applied to a big-box store located in Nashville, Tennessee, and a calibrated model was obtained that was used to study potential energy conservation measures. Further, the paper also explores the capabilities of whole-building energy modeling tools, such as EnergyPlus, for modeling the HVAC controls and sequences and their impact evaluation. Although some measures are precluded by the model simplicity, several measures were found to improve the efficiency of the model and demonstrate that the simplified modeling approach is effective. Practical Application: This paper introduces a hybrid approach of building energy model calibration using limited information available from the actual building in combination with characteristics of a “typical” building of the same type. This hybrid approach would also be applicable for other building types than discussed in this paper to calibrate the building energy model using limited information from the actual building.
The commercial building sector stock model, or ComStock™, is a highly granular, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual sub-hourly energy consumption of the commercial building stock across the United States. ComStock asks and answers two questions: how is energy used in the U.S. building stock and what are the impact of energy saving technologies. Specifically, ComStock identifies where energy is being consumed geographically, in what building types and end uses, and at what times of day. Simultaneously, it identifies the impact of efficiency measures: how much energy do efficiency measures save; where, or in what use cases do measures save energy; when, or at what time of day do savings occur; and which building stock segments have the biggest savings potential. This document contains the methodology and assumptions behind ComStock and serves as a guide to its use.
Executive Summary Building on the successfully completed effort to calibrate and validate the U.S. Department of Energy’s ResStock™ and ComStock™ models over the past several years, the objective of this work is to produce national datasets that empower analysts working for federal, state, utility, city, and manufacturer stakeholders to answer a broad range of analysis questions. The goal of this work is to develop energy efficiency, electrification, and demand flexibility end-use load shapes (electricity, gas, propane, or fuel oil) that cover most of the high-impact, market-ready (or nearly market-ready) measures. An end-use savings shape is the difference in energy consumption between a baseline building and a building with an energy efficiency, electrification, or demand flexibility measure applied. It results in a time series profile that is broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step. ComStock is a highly granular, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual sub-hourly energy consumption of the commercial building stock across the United States. The baseline model intends to represent the U.S. commercial building stock as it existed in 2018. This measure models the conversion of an existing heating, ventilating, and air-conditioning (HVAC) system to a series of “console” water-to-air heat pumps served by a ground heat exchanger. Console water-to-air geothermal heat pumps (GHP) are all-in-one packages that have no or minimal ductwork and serve individual spaces. Properly designed ground heat exchanger-coupled systems can offer benefits in energy efficiency relative to “conventional” HVAC systems, as well as facilitating beneficial electrification. Console GHPs can be coupled to a ground loop on the source side and can directly replace electric baseboard heaters or air-source packaged terminal heat pumps. Console GHPs can also bring in outdoor air for ventilation. This measure will be referred to throughout the document as the “Console GHP” upgrade.
In order to improve predictive skills of Earth System Model, we need to better understand processes that control Earth's energy budget via ocean, atmosphere, and cryosphere interactions. Simulated energy budget in comprehensive Earth System Models shows a wide range, leading to large uncertainties in predicting Earth system dynamic and thermodynamic variations and associated social-economic impacts. Uncertainties in cloud feedbacks have been identified as the main cause of the large inter-model spread, but oceanic adjustments, especially those associated with ocean heat uptake (OHU) and the Atlantic Meridional Overturning Circulation (AMOC), also play an important role. In this proposed work, we focus on understanding the individual and combined roles of cloud feedbacks and ocean adjustments on modulating Earth's energy balance. This research is motivated by our overarching hypothesis that oceanic adjustment is a key source of uncertainty, in addition to those associated with the cloud feedbacks; further, the ocean adjustment and associated OHU work through the cloud feedbacks to modulate Earth's energy budget and temperature variations. We test this hypothesis using numerical experiments where we systematically enable and disable cloud feedbacks in conjunction with perturbations to OHU.
Urban-scale building energy modeling (UBEM) holds promise for optimizing energy usage across extensive geographic regions. However, there is a recognized bias between simulated energy consumption and actual measured data. This study, based on building data from Chicago, delved into bias correction techniques for enhancing the accuracy of UBEM energy consumption estimates. Initially, the AutoBEM simulation yielded a normalized mean bias error (NMBE) of 1.1% and 51% of Coefficient of the Variation of the Root Mean Square Error (CVRMSE) after outlier exclusion. To address this, three bias correction methods were deployed: Average Mean Bias Error based bias correction, Quantile mapping bias correction, and Machine learning-based bias correction using Linear Regression and Random Forest models. Post-correction results exhibited marked improvement. The NMBE values were diminished to 0 for Average MBE-based, 0.36 for Quantile Mapping, and 0 for Machine Learning-based corrections. Concurrently, the CVRMSE values registered reductions from an original 51 to 50.8 for Quantile Mapping, and 38.56 for Machine Learning-based corrections, pointing towards the effectiveness of specific bias correction methods in refining the precision of UBEM energy predictions. Such accurate estimations are paramount for informed energy planning and urban policy-making.
Actin histidine N τ -methylation by histidine methyltransferase SETD3 plays an important role in human biology and diseases. Here, we report integrated synthetic, biocatalytic, biostructural, and computational analyses on human SETD3-catalyzed methylation of actin peptides possessing histidine and its structurally and chemically diverse mimics. Our enzyme assays supported by biostructural analyses demonstrate that SETD3 has a broader substrate scope beyond histidine, including N-nucleophiles on the aromatic and aliphatic side chains. Quantum mechanical/molecular mechanical molecular dynamics and free-energy simulations provide insight into binding geometries and the free energy barrier for the enzymatic methyl transfer to histidine mimics, further supporting experimental data that histidine is the superior SETD3 substrate over its analogs. This work demonstrates that human SETD3 has a potential to catalyze efficient methylation of several histidine mimics, overall providing mechanistic, biocatalytic, and functional insight into actin histidine methylation by SETD3.
Our goal is to compute a set of key performance indicators for commercially available smart thermostats using building energy simulation. If successful, the resulting tool will enable EPA ENERGY STAR and other rating/incentive programs to objectively evaluate new thermostat products.
The Multi-scenario Extreme Weather Simulator (MEWS) takes existing historical weather data and inserts trends and extreme events such as heat waves, hurricanes, extreme precipitation, or winter storms. The code includes functionality for projecting increasing frequency and duration of such events over a large range of statistically sampled realizations of the future. These weather files can then be used in building energy simulations through the Energy Plus software created by DOE. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525 SAND2021-8658 O