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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.

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DOE OSTI2024

Surrogate modelling for urban building energy simulation based on the bidirectional long short-term memory model

Here, the urban microclimate is essential for accurate simulation-based urban building energy modelling (UBEM). However, a high spatial-resolution microclimate can increase the computational resources demands of UBEM. Surrogate modelling is one of the promising approaches for fast UBEM. This study proposes a bidirectional Long Short-Term Memory (LSTM)-based approach for simulation-based UBEM surrogate modelling. The estimations are aggregated into census tracts using total building floor area. A case study using UBEM to estimate annual hourly building energy use and anthropogenic heat from all existing buildings in Los Angeles County found that most of the surrogate models can complete the annual hourly simulation within 90 minutes with a normalized mean absolute error lower than 10%, and that the bidirectional LSTM outperforms the standard LSTM in accuracy. This study demonstrates the advantages of bidirectional RNN architecture in building energy surrogate modelling and is expected to promote long-term and high-resolution UBEM with detailed microclimates.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

A Review and Outlook on Energy Consumption Estimation Models for Electric Vehicles

Electric vehicles (EVs) are critical to the transition to a low-carbon transportation system. The successful adoption of EVs heavily depends on energy consumption models that can accurately and reliably estimate electricity consumption. This paper reviews the state-of-the-art of EV energy consumption models, aiming to provide guidance for future development of EV applications. Here, we summarize influential variables of EV energy consumption into four categories: vehicle component, vehicle dynamics, traffic and environment related factors. We classify and discuss EV energy consumption models in terms of modeling scale (microscopic vs. macroscopic) and methodology (data-driven vs. rule-based). Our review shows trends of increasing macroscopic models that can be used to estimate trip-level EV energy consumption and increasing data-driven models that utilized machine learning technologies to estimate EV energy consumption based on large volume real-world data. We identify research gaps for EV energy consumption models, including the development of energy estimation models for modes other than personal vehicles (e.g., electric buses, electric trucks, and electric non-road vehicles); the development of energy estimation models that are suitable for applications related to vehicle-to-grid integration; and the development of multi-scale energy estimation models as a holistic modeling approach.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2005

National Aeronautics and Space Administration (NASA) Earth Science Research for Energy Management: Overview of Energy Issues and an Assessment of the Potential for Application of NASA Earth Science Research - Part 1

Effective management of energy resources is critical for the U.S. economy, the environment, and, more broadly, for sustainable development and alleviating poverty worldwide. The scope of energy management is broad, ranging from energy production and end use to emissions monitoring and mitigation and long-term planning. Given the extensive NASA Earth science research on energy and related weather and climate-related parameters, and rapidly advancing energy technologies and applications, there is great potential for increased application of NASA Earth science research to selected energy management issues and decision support tools. The NASA Energy Management Program Element is already involved in a number of projects applying NASA Earth science research to energy management issues, with a focus on solar and wind renewable energy and developing interests in energy modeling, short-term load forecasting, energy efficient building design, and biomass production.

Atmospheric models↗

DOE OSTI2016

Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings

Commercial buildings often experience faults that produce undesirable behavior in building systems. Building faults waste energy, decrease occupants' comfort, and increase operating costs. Automated fault detection and diagnosis (FDD) tools for buildings help building owners discover and identify the root causes of faults in building systems, equipment, and controls. Proper implementation of FDD has the potential to simultaneously improve comfort, reduce energy use, and narrow the gap between actual and optimal building performance. However, conventional rule-based FDD requires expensive instrumentation and valuable engineering labor, which limit deployment opportunities. This paper presents a hybrid, automated FDD approach that combines building energy models and statistical learning tools to detect and diagnose faults noninvasively, using minimal sensors, with little customization. We compare and contrast the performance of several hybrid FDD algorithms for a small security building. Our results indicate that the algorithms can detect and diagnose several common faults, but more work is required to reduce false positive rates and improve diagnosis accuracy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Enhanced Modeling of GHG Emissions and Mitigation in NEMS Component Design Report

The Office of Fossil Energy and Carbon Management (FECM), together with OnLocation, has developed a custom version of the National Energy Modeling System (NEMS), “FECM-NEMS”, that includes additional representation of energy- and industry-sector GHG emissions and mitigation options beyond those represented in the U.S. EIA's NEMS. Compared with GHG emissions published by the EPA in their Inventory of U.S. GHG Emissions and Sinks: 1990-2021, FECM-NEMS endogenously represents 81% of gross U.S. GHG emissions; however, the remaining 19%, as well as LULUCF-sector emissions and removals, are still required to properly model net-zero GHG scenarios, which requires an accounting of all GHGs. Considering recent technological advances to mitigate CO 2 and non-CO 2 emissions, the Office of Carbon Management (OCM) within FECM had tasked OnLocation with creating this component design report (CDR) to address the gap in GHG representation. This report describes how FECM-NEMS could incorporate missing GHG emissions (including LULUCF-sector emissions and removals) and engineered processes for GHG mitigation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Modeling Thermal Interactions between Buildings in an Urban Context

Thermal interactions through longwave radiation exchange between buildings, especially in a dense urban environment, can strongly influence a building’s energy use and environmental impact. However, these interactions are either neglected or oversimplified in urban building energy modeling. We developed a new feature in EnergyPlus to explicitly consider this term in the surface heat balance calculations and developed an algorithm to batch calculating the surrounding surfaces’ view factors using a ray-tracing technique. We conducted a case study with a district in the Chicago downtown area to evaluate the longwave radiant heat exchange effects between urban buildings. Results show that the impact of the longwave radiant effects on annual energy use ranges from 0.1% to 3.3% increase for cooling and 0.3% to 3.6% decrease for heating, varying among individual buildings. At the district level, the total energy demand increases by 1.39% for cooling and decreases 0.45% for heating. We also observe the longwave radiation can increase the exterior surface temperature by up to 10 °C for certain exterior surfaces. These findings justify a detailed and accurate way to consider the thermal interactions between buildings in an urban context to inform urban planning and design.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings: Preprint

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Modeling occupancy-driven building loads for large and diversified building stocks through the use of parametric schedules

Building energy modeling provides a fundamental tool to assess the potential for energy efficiency to contribute to reducing world energy consumption and global emissions. Occupancy-related operations are a key source of uncertainty for building energy analysis, particularly for aggregated building stocks. At a district or city level, it is critical to estimate aggregated power load profiles for sizing power grid infrastructure, power plant capacity allocation, and energy efficiency measures. The stochastic nature of behavior-related operations complicates the creation of models that accurately capture building load profiles for entire building stocks. This research introduces a new methodology called parametric schedules to model occupancy-driven schedules for large and diverse building stocks. In contrast to computationally expensive methodologies proposed in the literature, our work does not use a recursive time-consuming step. Occupancy is estimated by the extrapolation of operation times directly from metered electric consumption data; occupancy-related schedules are stochastically assigned to each building model, guaranteeing diversity of operation times in the stock. Our procedure has been tested on a large, diversified data-set of 25,000 commercial buildings in Los Angeles, California. It proved to be able to adequately represent the stochastic schedules diversity of the stock and to refine the stock calibration process by 1%. This innovative approach represents a useful asset for utility companies, grid operators, urban planners, and balancing authorities, seeking to improve building stock modeling and better estimate the impact of energy conservation measures. – This work is part of a larger stock modeling tool called ComStock, which is under development by NREL.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

The Life Cycle of an OpenStudio Measure: Development, Testing, Distribution, and Application

An OpenStudio Measure is a script that can manipulate an OpenStudio model and associated data to apply energy conservation measures (ECMs), run supplemental simulations, or visualize simulation results. The OpenStudio software development kit (SDK) and accessibility of the Ruby scripting language makes measure authorship accessible to both software developers and energy modelers. This paper discusses the life cycle of an OpenStudio Measure from development, testing, and distribution, to application.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

Zero Energy Ready Home Multifamily Case Study Analysis

Two multifamily buildings planned in Climate Zone 4 were analyzed to determine the cost, energy and performance implications of redesigning them to comply with Zero Energy Ready Home, a recognition program of the U.S. Department of Energy. Energy modeling was conducted on one representative apartment in each building using BEopt. Construction costs were obtained from the developer and subcontractors to determine savings and cost increases over ENERGY STAR. It was found that seven items would be necessary to change to comply with ZERH criteria when starting from the original design which was compliant with ENERGY STAR version 3.0. Design changes were made to the exterior walls, domestic water heating system, duct protection, duct design, garage ventilation, and pest control to comply with ZERH requirements. Energy impacts of upgrading from the original design to ZERH resulted in 2 to 8% reduction in modeled source energy consumption, or 1.7 to 10.4 MMBtu per year, although the original design was already about 8% better than a design configured to minimum ENERGY STAR criteria. According to the BEopt analysis, annualized energy related costs of the ZERH design were slightly higher for the apartment and slightly lower for the townhome when compared to the original design.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

Building America Case Study: Multifamily Zero Energy Ready Home Analysis, Elmsford, New York

Two multifamily buildings planned in Climate Zone 4 were analyzed to determine the cost, energy and performance implications of redesigning them to comply with Zero Energy Ready Home, a recognition program of the U.S. Department of Energy. Energy modeling was conducted on one representative apartment in each building using BEopt. Construction costs were obtained from the developer and subcontractors to determine savings and cost increases over ENERGY STAR. It was found that seven items would be necessary to change to comply with ZERH criteria when starting from the original design which was compliant with ENERGY STAR version 3.0. Design changes were made to the exterior walls, domestic water heating system, duct protection, duct design, garage ventilation, and pest control to comply with ZERH requirements. Energy impacts of upgrading from the original design to ZERH resulted in 2 to 8 percent reduction in modeled source energy consumption, or 1.7 to 10.4 MMBtu per year, although the original design was already about 8 percent better than a design configured to minimum ENERGY STAR criteria. According to the BEopt analysis, annualized energy related costs of the ZERH design were slightly higher for the apartment and slightly lower for the townhome when compared to the original design.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Evaluation of low-exergy heating and cooling systems and topology optimization for deep energy savings at the urban district level

District energy systems have the potential to achieve deep energy savings by leveraging the density and diversity of loads in urban districts. However, planning and adoption of district thermal energy systems is hindered by the analytical burden and high infrastructure costs. It is hypothesized that network topology optimization would enable wider adoption of advanced (ambient temperature) district thermal energy systems, resulting in energy savings. In this study, energy modeling is used to compare the energy performance of “conventional” and “advanced” district thermal energy systems at the urban district level, and a partial exhaustive search is used to evaluate a heuristic for the topology optimization problem. For the prototypical district considered, advanced district thermal energy systems mated with low-exergy building heating and cooling systems achieved a source energy use intensity that was 49% lower than that of conventional systems. The minimal spanning tree heuristic was demonstrated to be effective for the network topology optimization problem in the context of a prototypical district, and contributes to mitigating the problem’s computational complexity. The work presented in this paper demonstrates the potential of advanced district thermal energy systems to achieve deep energy savings, and advances to addressing barriers to their adoption through topology optimization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

Occupancy Simulator Solver v1.0

Occupant schedules at room level are crucial information to building energy modeling which quantifies occupant-related energy efficiency technologies and controls for new building design as well as existing building retrofits to reduce energy use and GHG emissions in buildings. The Occupancy Simulator Solver is an executable software component that simulates the stochastic behavior of occupant presence and movement in a building using the Markov-chain model. Occupancy Simulator Solver is the calculation engine of the web App Occupancy Simulator, which takes high level input on occupants, spaces and events, then calls the Solver to simulate occupant movement and generate occupant schedules for each space. The generated schedules capture diversity and stochastic nature of occupant activities (e.g., in office, meetings, breaks, lunch). These schedules are stored in CSV files and can be downloaded from the App and used for building performance simulation.

Hong, Tianzhen↗

DOE OSTI2023

Expanded modelling scenarios to understand the role of offshore wind in decarbonizing the United States

An assessment of decarbonization pathways in energy models reveals fundamental limitations in representing factors that are relevant for practical decision-making. Although these modelling limitations are widely acknowledged, their impact on the deployment of individual power generation types is not well understood. As a result, the societal value from such generation types could be vastly misrepresented. Here we explore a wide spectrum of factors that impact offshore wind deployment in the United States using a detailed capacity expansion model. Many factors prescribe a large future role for offshore wind, yet this diverges from what models often show. We extend the typically narrow modelling context through high spatial resolution, several cost and transmission possibilities and various energy-sector policies. Further, we estimate offshore wind to constitute 1-8% (31-256 gigawatts) of total US generation by 2050. This wide range suggests an uncertain but potentially important regional role. Our expansive scenarios demonstrate how to address many limitations of decarbonization modelling.

17 WIND ENERGY↗

DOE OSTI2020

Testing H 0 in Acoustic Dark Energy with Planck and ACT Polarization

The canonical acoustic dark energy model (cADE), which is based on a scalar field with a canonical kinetic term that rapidly converts potential to kinetic energy around matter radiation equality, alleviates the Hubble tension found in ΛCDM. Here, we show that it successfully passes new consistency tests in the CMB damping tail provided by the ACT data, while being increasingly constrained and distinguished from alternate mechanisms by the improved CMB acoustic polarization data from Planck. The best fit cADE model to a suite of cosmological observations, including the SH0ES H 0 measurement, has H 0 = 70.25 compared with 68.23 (km s –1 Mpc –1 ) in ΛCDM and a finite cADE component is preferred at the 2.8σ level. The ability to raise H0 is now mainly constrained by the improved Planck acoustic polarization data, which also plays a crucial role in distinguishing cADE from the wider class of early dark energy models. ACT and Planck TE polarization data are currently mildly discrepant in normalization and drive correspondingly different preferences in parameters. Improved constraints on intermediate scale polarization approaching the cosmic variance limit will be an incisive test of the acoustic dynamics of these models and their alternatives.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

Validating Savings Claims of Cold Climate Zero Energy Ready Homes

This study was intended to validate actual performance of three ZERHs in the Northeast to energy models created in REM/Rate v14.5 (one of the certified software programs used to generate a HERS Index) and the National Renewable Energy Laboratory’s Building Energy Optimization (BEopt™) v2.3 E+ (a more sophisticated hourly energy simulation software). This report details the validation methods used to analyze energy consumption at each home.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2018

An Open, Cloud-Based Platform for Whole-Building Fault Detection and Diagnostics

Small commercial buildings in the U.S. waste an estimated 300 Trillion BTU (approximately $6 billion in energy costs) annually due to faults, but lack cost-effective automated fault detection and diagnosis (AFDD) tools. NREL and GE Global Research are partnering to develop hybrid AFDD algorithms tailored to the unique needs of small commercial buildings (</=10,000 sq. ft.). The approach combines physics-based energy modeling tools with state-of-the-art machine learning algorithms to enable diagnostics using modeled input data. NREL, in partnership with Purdue University, has adapted DOE's physics-based modeling software EnergyPlus and OpenStudio to simulate common building faults. Cloud-based implementations of these physics-based models were used to generate data for multiple climate zones. The data were then used to train machine learning algorithms, such as those available in GE's Predix platform, to detect and diagnose building faults. Key features of the system including the automated, cloud-implementation of EnergyPlus, genetic algorithms for automated model feature selection, multiclass detection and diagnosis models, and anomaly detection algorithms will be presented.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Quality Control Methods for Advanced Metering Infrastructure Data

While urban-scale building energy modeling is becoming increasingly common, it currently lacks standards, guidelines, or empirical validation against measured data. Empirical validation necessary to enable best practices is becoming increasingly tractable. The growing prevalence of advanced metering infrastructure has led to significant data regarding the energy consumption within individual buildings, but is something utilities and countries are still struggling to analyze and use wisely. In partnership with the Electric Power Board of Chattanooga, Tennessee, a crude OpenStudio/EnergyPlus model of over 178,000 buildings has been created and used to compare simulated energy against actual, 15-min, whole-building electrical consumption of each building. In this study, classifying building type is treated as a use case for quantifying performance associated with smart meter data. This article attempts to provide guidance for working with advanced metering infrastructure for buildings related to: quality control, pathological data classifications, statistical metrics on performance, a methodology for classifying building types, and assess accuracy. Advanced metering infrastructure was used to collect whole-building electricity consumption for 178,333 buildings, define equations for common data issues (missing values, zeros, and spiking), propose a new method for assigning building type, and empirically validate gaps between real buildings and existing prototypes using industry-standard accuracy metrics.

24 POWER TRANSMISSION AND DISTRIBUTION↗