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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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At least 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

THE NATURE OF {gamma}-RAY LOUD NARROW-LINE SEYFERT I GALAXIES PKS 1502+036 AND PKS 2004-447

Variable {gamma}-ray emission has been discovered in five radio-loud narrow-line Seyfert 1 (NLSy1) galaxies by the Large Area Telescope on board the Fermi Gamma-ray Space Telescope. This has clearly demonstrated that these NLSy1 galaxies do have relativistic jets similar to two other cases of {gamma}-ray-emitting active galactic nuclei (AGNs), namely, blazars and radio galaxies. We present here our results on the multi-band analysis of two {gamma}-ray-emitting NLSy1 galaxies, namely, PKS 1502+036 (z = 0.409) and PKS 2004-447 (z = 0.240), using archival data. We generate multi-band long-term light curves of these sources, build their spectral energy distribution (SED), and model them using a one-zone leptonic model. They resemble more the SEDs of the flat spectrum radio quasar (FSRQ) class of AGNs. We then compare the SEDs of these two sources with two other Fermi-detected AGNs along the traditional blazar sequence, namely, the BL Lac Mrk 421 (z = 0.03) and the FSRQ 3C 454.3 (z = 0.86). The SEDs of both PKS 1502+036 and PKS 2004-447 are found to be intermediate to the SEDs of Mrk 421 and 3C 454.3. In the {gamma}-ray spectral index versus {gamma}-ray luminosity plane, both these NLSy1 galaxies occupy a distinct position, wherein they have luminosity between Mrk 421 and 3C 454.3; however, their steep {gamma}-ray spectra are similar to 3C 454.3. Their Compton dominance as well as their X-ray spectral slope also lie between Mrk 421 and 3C 454.3. We argue that the physical properties of both PKS 1502+036 and PKS 2004-447 are generally similar to blazars and intermediate between FSRQs and BL Lac objects and these sources thus could fit into the traditional blazar sequence.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Modelica IBPSA MPC Library v0.1

The Modelica IBPSA MPC Library is a free open-source library with basic models that codify best practices for the implementation of models to be used in Nonlinear Model Predictive Control of building and community energy systems. The development of the Modelica IBPSA MPC Library is organized through the IBPSA Project 1 (https://ibpsa.github.io/project1) of the International Building Performance Simulation Association (IBPSA).

Blum, David↗
DOE OSTI2014

Smart and Green Energy (SAGE) for Base Camps Final Report

The U.S. Army Logistics Innovation Agency’s (LIA’s) Smart and Green Energy (SAGE) for Base Camps project was to investigate how base camps’ fuel consumption can be reduced by 30% to 60% using commercial off-the-shelf (COTS) technologies for power generation, renewables, and energy efficient building systems. Field tests and calibrated energy models successfully demonstrated that the fuel reductions are achievable.

energy↗
DOE OSTI2015

High Performance Walls in Hot-Dry Climates

High performance walls represent a high priority measure for moving the next generation of new homes to the Zero Net Energy performance level. The primary goal in improving wall thermal performance revolves around increasing the wall framing from 2x4 to 2x6, adding more cavity and exterior rigid insulation, achieving insulation installation criteria meeting ENERGY STAR's thermal bypass checklist, and reducing the amount of wood penetrating the wall cavity.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

High Performance Walls in Hot-Dry Climates

High performance walls represent a high priority measure for moving the next generation of new homes to the Zero Net Energy performance level. The primary goal in improving wall thermal performance revolves around increasing the wall framing from 2x4 to 2x6, adding more cavity and exterior rigid insulation, achieving insulation installation criteria meeting ENERGY STAR's thermal bypass checklist. To support this activity, in 2013 the Pacific Gas & Electric Company initiated a project with Davis Energy Group (lead for the Building America team, Alliance for Residential Building Innovation) to solicit builder involvement in California to participate in field demonstrations of high performance wall systems. Builders were given incentives and design support in exchange for providing site access for construction observation, cost information, and builder survey feedback. Information from the project was designed to feed into the 2016 Title 24 process, but also to serve as an initial mechanism to engage builders in more high performance construction strategies. This Building America project utilized information collected in the California project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Beyond Benchmarking : Unlocking the Value for Utilities

This document provides guidance to help utilities identify new, untapped datasets related to the energy performance of buildings, and how this information can be applied to expand market intelligence and create business value.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Multidisciplinary concurrent optimization framework for multi-phase building design process

Modern day building design projects require multidisciplinary expertise from architects and engineers across various phases of the design (conceptual, preliminary, and detailed) and construction processes. The Architecture Engineering and Construction (AEC) community has recently shifted gears toward leveraging design optimization techniques to make well-informed decisions in the design of buildings. However, most of the building design optimization efforts are either multidisciplinary optimization confined to just a specific design phase (conceptual/preliminary/detailed) or single disciplinary optimization (structural/thermal/daylighting/energy) spanning across multiple phases. Complexity in changing the optimization setup as the design progresses through subsequent phases, interoperability issues between modeling and physics-based analysis tools used at later stages, and the lack of an appropriate level of design detail to get meaningful results from these sophisticated analysis tools are few challenges that limit multi-phase multidisciplinary design optimization (MDO) in the AEC field. Here this paper proposes a computational building design platform leveraging concurrent engineering techniques such as interactive problem structuring, simulation-based optimization using meta models for energy and daylighting (machine learning based) and tradespace visualization. The proposed multi-phase concurrent MDO framework is demonstrated by using it to design and optimize a sample office building for energy and daylighting objectives across multiple phases. Furthermore, limitations of the proposed framework and future avenues of research are listed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Development of Fault Models for Hybrid Fault Detection and Diagnostics Algorithm

This report describes models of building faults created for OpenStudio to support the ongoing development of fault detection and diagnostic (FDD) algorithms at the National Renewable Energy Laboratory. Building faults are operating abnormalities that degrade building performance, such as using more energy than normal operation, failing to maintain building temperatures according to the thermostat set points, etc. Models of building faults in OpenStudio can be used to estimate fault impacts on building performance and to develop and evaluate FDD algorithms.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Learning-based framework for sensor fault-tolerant building HVAC control with model-assisted learning

As people spend up to 87% of their time indoors, intelligent Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings are essential for maintaining occupant comfort and reducing energy consumption. These HVAC systems in smart buildings rely 'on real-time sensor readings, which in practice often suffer from various faults and could also be vulnerable to malicious attacks. Such faulty sensor inputs may lead to the violation of indoor environment requirements (e.g., temperature, humidity, etc.) and the increase of energy consumption. While many model-based approaches have been proposed in the literature for building HVAC control, it is costly to develop accurate physical models for ensuring their performance and even more challenging to address the impact of sensor faults. In this work, we present a novel learning-based framework for sensor fault-tolerant HVAC control, which includes three deep learning based components for 1) generating temperature proposals with the consideration of possible sensor faults, 2) selecting one of the proposals based on the assessment of their accuracy, and 3) applying reinforcement learning with the selected temperature proposal. Moreover, to address the challenge of training data insufficiency in building-related tasks, we propose a model-assisted learning method leveraging an abstract model of building physical dynamics. Through extensive experiments, we demonstrate that the proposed fault-tolerant HVAC control framework can significantly reduce building temperature violations under a variety of sensor fault patterns while maintaining energy efficiency.

Xu, Shichao↗
DOE OSTI2022

Potential Heating Energy and Cost Savings of Dual Fuel Heat Pump Controls as a Residential Building Equipment Retrofit in the U.S.

This study investigated the energy and cost savings potential by modeling a dual fuel heat pump system with its control for a residential building in (5A) cold climate in U.S. The simulation analysis showed that the application of the DFHP system and its control to the target building can save the heating energy by 42% over a gas furnace system (baseline system). Although the HP-only system can save more heating energy (i.e.,53%) over the baseline system than DFHP system does, the DFHP with its control demonstrated the higher cost savings when a time of use electricity rate was considered.

Dual Fuel Heat Pump, Control, Residential Building↗
DOE OSTI2015

Energy and Energy Cost Savings Analysis of the 2015 IECC for Commercial Buildings

As required by statute (42 USC 6833), DOE recently issued a determination that ANSI/ASHRAE/IES Standard 90.1-2013 would achieve greater energy efficiency in buildings subject to the code compared to the 2010 edition of the standard. Pacific Northwest National Laboratory (PNNL) conducted an energy savings analysis for Standard 90.1-2013 in support of its determination . While Standard 90.1 is the model energy standard for commercial and multi-family residential buildings over three floors (42 USC 6833), many states have historically adopted the International Energy Conservation Code (IECC) for both residential and commercial buildings. This report provides an assessment as to whether buildings constructed to the commercial energy efficiency provisions of the 2015 IECC would save energy and energy costs as compared to the 2012 IECC. PNNL also compared the energy performance of the 2015 IECC with the corresponding Standard 90.1-2013. The goal of this analysis is to help states and local jurisdictions make informed decisions regarding model code adoption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Building Life-Cycle Analysis with the GREET Building Module: Methodology, Data, and Case Studies

To holistically address building sustainability, Argonne National Laboratory has expanded its Greenhouse gases, Regulated Emissions, and Energy use in Technologies (GREET) life-cycle model with a new GREET Building Module. This report documents life-cycle analysis (LCA) methodology and foreground data that Argonne National Laboratory compiles and develops to address embodied greenhouse gas (GHG) emissions and energy impacts of a wide range of envelope and structural building materials for new construction and retrofits. The methodology and data form the backbone of the GREET Building Module. This research effort focuses on developing consistent LCA methodology that conforms to building LCA standards such as the EN 15978 to address embodied GHG emissions and energy impacts of building materials/technologies. We document detailed foreground data for selected building materials and building components that are common for building construction. To test the LCA methodology and the GREET Building Module, this report includes case studies of insulation materials and wall panels for residential building retrofit. We have developed a separate document as a User Guide for understanding and applying the GREET Building Module to conduct detailed, process-level LCA of embodied carbon and energy impacts of emerging building materials and technology solutions that of interest to the Building Technologies Office (BTO) of the US Department of Energy, researchers, and industry stakeholders.

42 ENGINEERING↗
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 OSTI2013

Cold Climate Foundation Retrofit Energy Savings: The Simulated Energy and Experimental Hygrothermal Performance of Cold Climate Foundation Wall Insulation Retrofit Measures — Phase I, Energy Simulation

A split simulation whole-building energy/three-dimensional earth contact model (termed the BUFETS/EnergyPlus Model or BEM) can model the full range of foundation systems in the target retrofit housing stock. This model was used to extensively evaluate the results produced by the NREL BEoptE+ tool. These foundation systems that include above-grade foundation walls, diabatic floors or slabs, and lookout or walkout walls could not be modeled in BEopt version 1.1 that was used in this project.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Quantifying the effect of multiple load flexibility strategies on commercial building electricity demand and services via surrogate modeling

The expansion of commercial building demand response as a demand-side management resource for the electric grid necessitates new decision support resources for customers seeking to assess the benefit–risk tradeoffs of possible strategies for energy flexible building operations. To address this need, we, in this study, develop surrogate models that predict the impacts of several load flexibility strategies on commercial building electricity demand and indoor temperature, focusing on offices and retail buildings at multiple scales. The surrogate models are fit to a synthetic database generated via whole building simulations, which establish the relationships between the key operational features of a given strategy and potential changes in building demand and temperature across a variety of contexts. The surrogate models are translated to a Bayesian framework to allow straightforward communication of uncertainty and parameter updating given new evidence. We find strong predictive performance across the suite of models, underscoring the usefulness of the approach in guiding decisions about implementing load flexibility strategies under a particular set of operational and environmental conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗