Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Building Energy Modeling”

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.

At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

An Analysis of Representative Heating Load Lines for Residential HSPF Ratings

This report describes an analysis to investigate representative heating loads for single-family detached homes using current EnergyPlus simulations (DOE 2014a). Hourly delivered load results are used to determine binned load lines using US Department of Energy (DOE) residential prototype building models (DOE 2014b) developed by Pacific Northwest National Laboratory (PNNL). The selected residential single-family prototype buildings are based on the 2006 International Energy Conservation Code (IECC 2006) in the DOE climate regions. The resulting load lines are compared with the American National Standards Institute (ANSI)/Air-Conditioning, Heating, and Refrigeration Institute (AHRI) Standard 210/240 (AHRI 2008) minimum and maximum design heating requirement (DHR) load lines of the heating seasonal performance factor (HSPF) ratings procedure for each region. The results indicate that a heating load line closer to the maximum DHR load line, and with a lower zero load ambient temperature, is more representative of heating loads predicted for EnergyPlus prototype residential buildings than the minimum DHR load line presently used to determine HSPF ratings. An alternative heating load line equation was developed and compared to binned load lines obtained from the EnergyPlus simulation results. The effect on HSPF of the alternative heating load line was evaluated for single-speed and two-capacity heat pumps, and an average HSPF reduction of 16% was found. The alternative heating load line relationship is tied to the rated cooling capacity of the heat pump based on EnergyPlus autosizing, which is more representative of the house load characteristics than the rated heating capacity. The alternative heating load line equation was found to be independent of climate for the six DOE climate regions investigated, provided an adjustable zero load ambient temperature is used. For Region IV, the default DOE climate region used for HSPF ratings, the higher load line results in an ~28% increase in delivered heating load and an ~52% increase in the estimated heating operating cost over that given in the AHRI directory (AHRI 2014).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Identification Approach to Alleviate Effects of Unmeasured Heat Gains for MIMO Building Thermal Systems

It is important to have practical methods for constructing a good mathematical model for a building's thermal system for energy audits, retrofit analysis and advanced building controls, e.g. model predictive control. Identification approaches based on semi-physical model structures are popular in building science for those purposes. However conventional gray box identification approaches applied to thermal networks would fail when significant unmeasured heat gains present in estimation data. Although this situation is very common and practical, there has been little research to tackle this issue in building science. This paper presents an overall identification approach to alleviate influences of unmeasured disturbances, and hence to obtain improved gray-box building models. The approach was applied to an existing open space building and the performance is demonstrated.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Building thermal dynamics modeling with deep transfer learning using a large residential smart thermostat dataset

Understanding thermal dynamics and obtaining the computational model of residential buildings enable its scaled application in energy retrofits, control optimization and decarbonization. In this paper, we present a deep learning approach to model building thermal dynamics with smart thermostat data collected from residential buildings, with the goal to investigate model generalizability. In the first stage, we developed and compared different Deep Learning architectures including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) models and CNN-LSTM to predict indoor air temperature in a multi-step time horizon. In the second stage, we implemented a Transfer Learning (TL) process, which aims to improve the prediction performance on a new set of buildings (targets), exploiting the knowledge of related or similar buildings (sources). Different TL strategies and source model identification methods were investigated. The study showed that the CNN-LSTM performed the best among the architectures compared, with an average Mean Absolute Error (MAE) of 0.26 °C for one-hour-ahead (twelve 5-min future steps) predictions. Furthermore, the results showed that freezing the LSTM layer and fine-tuning the other layers of the CNN-LSTM achieved the best performance among four TL strategies, which further improved the performance with respect to a machine learning approach by 10%, and proving the effectiveness and generalizability of the proposed approach. A comparison of three different source model identification methods showed that randomly selecting source models constrained by similar building characteristics can provide good TL performance while retaining simplicity comparing with other quantitative source identification methods.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Optimizing district energy systems under uncertainty: Insights from a case study from Washington D.C., USA

This study investigates solutions for delivering affordable heating and cooling to a brownfield site, focusing on a case study in Washington, DC. Moving towards more diverse and resilient energy systems, we identify the optimal portfolio for a district energy system with diverse energy sources to meet the area’s energy demands. Our methodological approach integrates two detailed models: one calculating building-level energy demand and the other optimizing district energy technology choices based on their demand profiles, accounting for uncertainties in energy prices, policies, and other parameters. The results provide an economic comparison of district and individual supply options at the building level, emphasizing the flexibility district systems can offer to the electricity sector. District energy systems demonstrate cost-stabilization benefits amidst volatile energy prices and external uncertainties. For heating, district systems yield significant cost savings compared to individual solutions, driven by fuel flexibility and the use of local renewable energy sources. For cooling, district systems also show advantages, though individual systems may remain more cost-effective for smaller buildings. Additionally, district systems exhibit considerable flexibility on the heating side, as evidenced by variations in electricity consumption. We recommend future research to explore the relationship between the economics of district energy systems, particularly at the building level, and their flexibility potential for the electricity sector across diverse geographic contexts to reduce overall grid costs and promote grid reliability. This includes areas with distinct zoning laws, municipal priorities, utility structures, and funding mechanisms, such as the United States, and regions like Europe with pronounced electricity price volatility.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

The New Face of Standard 100

In January 2024, ASHRAE published a significant overhaul to ANSI/ASHRAE/IES Standard 100-2018, Energy Efficiency in Existing Buildings. You may almost not recognize it. Standard 100-2024, Energy and Emissions Building Performance Standard for Existing Buildings, now addresses both energy and emissions performance as a model building performance standard (BPS). ASHRAE Standing Standard Project Committee (SSPC) 100, with direction from ASHRAE and its Task Force for Building Decarbonization (TFBD) has been working to update the standard to make it a model for building performance standard adoption across the U.S. and internationally. Here, the new edition of Standard 100 addresses key issues of climate change surrounding building performance, including new metrics for greenhouse gas (GHG) emissions targets, continuing to support the imperative of improving our existing building stock and meeting global climate change commitments.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

New Whole-House Solutions Case Study: Hood River Passive House

The Hood River Passive Project was developed by Root Design Build of Hood River Oregon using the Passive House Planning Package (PHPP) to meet all of the requirements for certification under the European Passive House standards. The Passive House design approach has been gaining momentum among residential designers for custom homes and BEopt modeling indicates that these designs may actually exceed the goal of the U.S. Department of Energy's (DOE) Building America program to "reduce home energy use by 30%-50%" (compared to 2009 energy codes for new homes). This report documents the short term test results of the Shift House and compares the results of PHPP and BEopt modeling of the project. The design includes high R-Value assemblies, extremely tight construction, high performance doors and windows, solar thermal DHW, heat recovery ventilation, moveable external shutters and a high performance ductless mini-split heat pump. Cost analysis indicates that many of the measures implemented in this project did not meet the BA standard for cost neutrality. The ductless mini-split heat pump, lighting and advanced air leakage control were the most cost effective measures. The future challenge will be to value engineer the performance levels indicated here in modeling using production based practices at a significantly lower cost.

BA-PIRC↗
DOE OSTI2018

Calibrated simulation analysis for integration of evaporative cooling and radiant cooling system for different Indian climatic zones

Radiant cooling system (RCS) has proven to be an energy efficient system for meeting a building's cooling requirements. RCS is energy efficient and provides better thermal comfort compared to conventional all-air heating ventilation air conditioning (HVAC) system. To further improve the efficiency of RCS, a parallel evaporative cooling system (cooling tower) is coupled with RCS and analyzed for different climatic condition using calibrated model. A statistical analysis of weather files (based on wet bulb temperature), was used to identify the availability of useful water for cooling tower integration with RCS. A comprehensive simulation feasibility study of the application of cooling tower in RCS was performed for different cities to cover every climatic zones of India. It was found that in summer, the wet bulb temperature (WBT) of different climatic zones, except warm-humid, is suitable for the integration of cooling tower with RCS. An experimental setup was designed and developed for integration of parallel chiller and cooling tower with panel-based RCS for cooling and a dedicated outdoor air system (DOAS) for dehumidification and ventilation. Experiments were conducted for chiller and cooling tower operated RCS in Composite climate of Jaipur, Rajasthan, India. Building Energy Models (BEM) were developed for both the chiller-operated RCS and cooling tower-operated RCS in EnergyPlus and calibrated with the measured data. Using the calibrated models, performance of the system was analyzed for different climatic zones of India. A chiller-operated RCS was considered as a baseline to compare the annual energy saving potential and monthly performance of the cooling tower integrated RCS. Here, in the cooling tower–operated RCS, a total annual savings of 7% in hot and dry climates, 11% in composite climates and 20% in temperate climates was achieved compared to the chiller-operated RCS.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

User-Oriented Modeling Tools for Advanced Hybrid and Climate-Appropriate Rooftop Air Conditioners

Hybrid unitary air conditioning systems offer a pathway to substantially reduce energy use and peak electrical demand for cooling, heating, and ventilation in commercial buildings. Hybrid air conditioners incorporate multiple subsystems that are carefully orchestrated to provide climate- and application-specific efficiency advantages. There are a multitude of hybrid system architectures, but common subsystems include: heat recovery ventilation, indirect evaporative cooling, desiccant dehumidification, variable speed fans, modulating dampers, and multi-stage or variable-speed vapor compression cooling. Categorically, hybrid systems can operate in numerous discrete modes. For example: indirect evaporative cooling may operate for periods when the subsystem provides adequate sensible cooling, then vapor compression cooling will be included when more cooling or dehumidification is necessary. Laboratory assessments, field studies, and simulations have demonstrated that hybrid unitary air conditioners could reduce energy use for cooling and ventilation by 30-90% depending on climate and application. Heretofore, it has been challenging - if not impossible - for practitioners to model hybrid air conditioners as part of building energy simulations; and the limitation has severely obstructed broader adoption of technologies in this class. In this project, we developed a new feature for EnergyPlus that enables modeling hybrid unitary air conditioning equipment for building energy simulations. This is a significant advancement for both theory and practice, and confers public benefit by enabling practitioners to evaluate this compelling efficiency technology as a part of building energy simulations. The feature is a black-box model that requires extensive performance data for each hybrid unitary product. In parallel, we also developed new features for the Technology Performance Exchange to enable manufacturers to submit performance data in a standard format that can be used with the hybrid unitary model in EnergyPlus. Additionally, through this project we expanded university educational resources, and university- manufacturing industry collaborations in the field of energy efficiency technology. Over two years, we involved 20 undergraduate students in ambitious research projects focused on modeling complex multi-mode mechanical systems, supported three mechanical engineering bachelor theses, established undergraduate apprenticeships with multiple industry partners, and involved those partners in the process of design, validation, and debugging for the new EnergyPlus feature. The EnergyPlus feature is described and discussed in an academic article, as well as in an engineering reference, and input/output reference documentation for EnergyPlus. The Technology Performance Exchange features are live and publicly accessible, our manufacturer partners are primed to submit initial product information and performance data to the exchange, and the EnergyPlus feature is scheduled for public release in Spring 2018 as a part of EnergyPlus v8.9.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

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.

big data↗
DOE OSTI2016

Thermostatic Radiator Valve Evaluation and Demonstration

A large stock of multifamily buildings in the Northeast and Midwest uses hot water or steam for space heating. Typically, residents do not pay for heat directly (i.e., heating fuel serves a central plant and use is not submetered). Losses from these systems are typically high, and a significant number of apartments are overheated much of the time. This is often evidenced by open windows on winter days. Controls and distribution are often faulty, and improving them can be more cost effective than replacing boilers. Thermostatic radiator valves (TRVs), which have been in use for many decades, are one potential strategy to combat this problem. They are commonly used in Europe and in other markets such as commercial buildings, but have not been widely accepted by the residential retrofit market in the northeastern United States. Anecdotal evidence suggests that heating systems engineers and contractors have a variety of opinions about their effectiveness, illustrating a lack of consensus on this potentially important energy efficiency measure. A review of the limited available literature revealed that, in one study, heating fuel savings as high as 15% was achieved through TRV retrofits. In this project, the U.S. Department of Energy Building America team, Advanced Residential Integrated Energy Solutions, sought to better understand the current usage of TRVs by key market players in steam and hot water heating and to conduct limited experiments on the effectiveness of new and old TRVs as a means of controlling space temperatures and reducing heating fuel consumption. The project included a survey of industry professionals, a field experiment comparing old and new TRVs, and cost-benefit modeling analysis using BEopt (Building Energy Optimization software). Radiator and apartment space temperature data were collected and analyzed for two similar apartment units in a building that underwent a one-pipe steam TRV retrofit. Space temperature comparisons were made across the pre- and post-TRV installation heating periods and between rooms equipped with old or new TRVs in an attempt to show the comparative effectiveness of each vintage of TRV. Analyses of the heating fuel utility bills before and after the building-wide TRV installation were conducted to quantify potential savings. The results of the field experiment and utility bill analysis did not show energy savings at either the unit or the building-wide level. The results provided inconclusive answers to the original study questions but provided valuable insight into common steam system imbalance and resident behavior issues that are critical to address in conjunction with TRV retrofits. Specific issues identified included steam distribution imbalance, possible TRV sensor location issues, a persistent window-opening habit, and a failure to optimize the boiler control set points as part of the TRV retrofit. The lack of heating fuel savings underscored the need to include whole steam system commissioning alongside or as a prerequisite to TRVs. Failed air vents and uneven steam main venting are critical to address either in conjunction with or before a TRV installation. Monitoring existing space temperatures before a retrofit strategy is chosen would allow the consultant and building owner to better assess the potential benefits of a whole-building TRV retrofit, selective installation of TRVs in some units, or simply balancing the steam distribution venting. Further gaps in the understanding of underlying processes that allow TRVs to function effectively were also identified and are presented as opportunities for future research.

Array↗
DOE OSTI2019

An Innovative Fault Impact Analysis Framework for Enhancing Building Operations

The main goal of this paper is to rank building faults based on their impacts on the building energy penalty and occupant thermal comfort penalty considering multiple faults and fault occurrence rates. A fault impact analysis framework is created by incorporating the fault model library with the whole building energy performance simulation (e.g., EnergyPlus used in this study). The fault occurrence rate is introduced as a 'meta' parameter in the simulation. This framework involves three essential aspects of conducting a fault impact analysis: fault constructing, fault simulation, and fault impact analysis. A parametric sensitivity analysis was used to determine and rank the criticality of the faults considering the fault concurrence frequency, by using the deep-learning based response surface model (i.e., the multi-layer perceptron regression). The proposed fault analysis framework with ranking was tested and demonstrated for the DOE's prototype medium-sized office in four different climate zones (i.e., Atlanta, Chicago, Miami, and San Francisco) with 12,000 EnergyPlus fault simulations. A total of 129 fault modes from 41 groups of fault models were simulated for the medium-sized office case. The results demonstrate the proposed framework is robust and scalable for the fault impact analysis. The top critical fault for the medium-sized office is the fault of HVAC-Left-ON for the packaged rooftop unit, regarding the site energy, source energy, and HVAC energy. Excluding the fault of HVAC-Left-ON, the top critical faults vary significantly among the four climate zones.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Improving Characterization of Miscellaneous Energy Loads in Energy Demand Models

Miscellaneous energy loads (MELs) comprise a significant and growing portion of total building energy consumption. However relatively little is known about the products that make up MELs end uses, and MELs are modeled with less granularity than major end uses in energy demand models such as the U.S. Department of Energy’s Scout energy efficiency impact analysis tool or the U.S. Energy Information Administration’s National Energy Modeling System (NEMS). This paper identifies differences in the way MELs versus major building end uses are modeled, and then reviews potential sources of MELs baseline technology data that could be used to improve their characterization in energy demand models. Case studies of dry distribution transformers, hot water circulation pumps, and Japanese-style automatic bidets are presented to compare and contrast the level of baseline data available for different end uses. We show that there is potential to use data from energy conservation standards and other sources to significantly improve representation of some MELs in energy demand models, while other emerging MELs require further data collection before they can be modeled precisely.

Miscellaneous Electric Loads, MELs, Models, Nation↗
DOE OSTI2019

Improving Characterization of Miscellaneous Energy Loads in Energy Demand Models

Miscellaneous energy loads (MELs) comprise a significant and growing portion of total building energy consumption. However relatively little is known about the products that make up MELs end uses, and MELs are modeled with less granularity than major end uses in energy demand models such as the U.S. Department of Energy's Scout energy efficiency impact analysis tool or the U.S. Energy Information Administration's National Energy Modeling System (NEMS). This paper identifies differences in the way MELs versus major building end uses are modeled, and then reviews potential sources of MELs baseline technology data that could be used to improve their characterization in energy demand models. Case studies of dry distribution transformers, hot water circulation pumps, and Japanese-style automatic bidets are presented to compare and contrast the level of baseline data available for different end uses. We show that there is potential to use data from energy conservation standards and other sources to significantly improve representation of some MELs in energy demand models, while other emerging MELs require further data collection before they can be modeled precisely.

Fares, Robert↗
DOE OSTI2015

A concept of integrated environmental approach for building upgrades and new construction: Part 1—setting the stage

This article highlights the need for an active role for building physics in the development of near-zero energy buildings while analyzing an example of an integrated system for the upgrade of existing buildings. The science called either Building Physics in Europe or Building Science in North America has so far a passive role in explaining observed failures in construction practice. In its new role, it would be integrating modeling and testing to provide predictive capability, so much needed in the development of near-zero energy buildings. The authors attempt to create a compact package, applicable to different climates with small modifications of some hygrothermal properties of materials. This universal solution is based on a systems approach that is routine for building physics but in contrast to separately conceived sub-systems that are typical for the design of buildings today. One knows that the building structure, energy efficiency, indoor environmental quality, and moisture management all need to be considered to ensure durability of materials and control cost of near-zero energy buildings. These factors must be addressed through contributions of the whole design team. The same approach must be used for the retrofit of buildings. As this integrated design paradigm resulted from demands of sustainable built environment approach, building physics must drop its passive role and improve two critical domains of analysis: (i) linked, real-time hygrothermal and energy models capable of predicting the performance of existing buildings after renovation and (ii) basic methods of indoor environment and moisture management when the exterior of the building cannot be modified.

Building physics↗
DOE OSTI2020

City and County Commercial Building Inventories

The Commercial Building Inventories provide modeled data on commercial building type, vintage, and area for each U.S. city and county. Please note this data is modeled and more precise data may be available through county assessors or other sources. Commercial building stock data is estimated using CoStar Realty Information, Inc. building stock data. This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and builds on Cities-LEAP energy modeling, available at the "EERE Cities-LEAP Page" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

Array↗
DOE OSTI2014

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.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives

Smart buildings play a crucial role toward decarbonizing society, as globally buildings emit about one-third of greenhouse gases. In the last few years, machine learning has achieved a notable momentum that, if properly harnessed, may unleash its potential for advanced analytics and control of smart buildings, enabling the technique to scale up for supporting the decarbonization of the building sector. In this perspective, transfer learning aims to improve the performance of a target learner exploiting knowledge in related environments. The present work provides a comprehensive overview of transfer learning applications in smart buildings, classifying and analyzing 77 papers according to their applications, algorithms, and adopted metrics. The study identified four main application areas of transfer learning: (1) building load prediction, (2) occupancy detection and activity recognition, (3) building dynamics modeling, and (4) energy systems control. Furthermore, the review highlighted the role of deep learning in transfer learning applications that has been used in more than half of the analyzed studies. The paper also discusses how to integrate transfer learning in a smart building's ecosystem, identifying, for each application area, the research gaps and guidelines for future research directions.

Pinto, G↗