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 775 records · Page 43

Evaluation of the Impact of Slab Foundation Heat Transfer on Heating and Cooling in Florida

During the last three decades of energy-efficiency research, there has been limited study of heat transfer to slab-on-grade foundations in cooling-dominated climates. Most experimental research has focused on the impact of slab-on-grade foundations and insulation schemes on heat losses in heating-dominated climates. This is surprising because the floor area in single-family homes is generally equal to wall area, window area, or attic area, all of which have been extensively evaluated for heat-transfer properties. Moreover, slab foundations are the most common foundation type in cooling-dominated climates. Slab-on-grade construction is very popular in southern states, accounting for 77% of new home floors according to 2014 U.S. Census data. There is a widespread perception that tile flooring, as opposed to carpet, provides a cooler home interior in warm climates. Empirical research is needed because building energy simulation software programs running DOE-2 and EnergyPlus engines often rely on simplified models to evaluate the influence of flooring on interior temperature, even though in some cases more detailed models exist. The U.S. Department of Energy Building America Partnership for Improved Residential Construction (BA-PIRC) performed experiments in the Florida Solar Energy Center’s Flexible Residential Test Facility intended to assess for the first time (1) how slab-on-grade construction influences interior cooling in a cooling-dominated climate and (2) how the difference in a carpeted versus uncarpeted building might influence heating and cooling energy use. Two nominally identical side-by-side residential buildings were evaluated during the course of 1 year, from 2014 to 2015: the east building with a pad and carpet floor and the west building with a bare slab floor. A detailed grid shows temperature measurements taken on the slab surface at various locations as well as at depths of 1.0 ft, 2 ft, 5.0 ft, 10.0 ft, and 20.0 ft below the surface. Temperature measurements were taken at both buildings for more than 3 years prior to the experiments to ensure that the ground and foundation temperatures had fully come into equilibrium.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Evaluation of the Impact of Slab Foundation Heat Transfer on Heating and Cooling in Florida

During the last three decades of energy-efficiency research, there has been limited study of heat transfer to slab-on-grade foundations in cooling-dominated climates. Most experimental research has focused on the impact of slab-on-grade foundations and insulation schemes on heat losses in heating-dominated climates. This is surprising because the floor area in single-family homes is generally equal to wall area, window area, or attic area, all of which have been extensively evaluated for heat-transfer properties. Moreover, slab foundations are the most common foundation type in cooling-dominated climates. Slab-on-grade construction is very popular in southern states, accounting for 77% of new home floors according to 2014 U.S. Census data. There is a widespread perception that tile flooring, as opposed to carpet, provides a cooler home interior in warm climates. Empirical research is needed because building energy simulation software programs running DOE-2 and EnergyPlus engines often rely on simplified models to evaluate the influence of flooring on interior temperature, even though in some cases more detailed models exist. The U.S. Department of Energy Building America Partnership for Improved Residential Construction (BA-PIRC) performed experiments in the Florida Solar Energy Center’s Flexible Residential Test Facility intended to assess for the first time (1) how slab-on-grade construction influences interior cooling in a cooling-dominated climate and (2) how the difference in a carpeted versus uncarpeted building might influence heating and cooling energy use. Two nominally identical side-by-side residential buildings were evaluated during the course of 1 year, from 2014 to 2015: the east building with a pad and carpet floor and the west building with a bare slab floor. A detailed grid shows temperature measurements taken on the slab surface at various locations as well as at depths of 1.0 ft, 2 ft, 5.0 ft, 10.0 ft, and 20.0 ft below the surface. Temperature measurements were taken at both buildings for more than 3 years prior to the experiments to ensure that the ground and foundation temperatures had fully come into equilibrium.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Modelica-json: Transforming energy models to digitize the control delivery process

Building simulation models are typically not used to generate the documentation required for bidding and project delivery of commercial building systems, or for their semantic modeling and commissioning. This paper presents a software tool that aids in digitizing the control delivery process, spanning simulation during design to implementation and formal verification during commissioning. The tool can generate from Modelica models digital documentation of control sequences. This digital documentation, along with other project drawings and specifications can be used for project bidding. It can also be used for implementation of control sequences through machineto-machine translation to commercial legacy control products, for which we are currently developing the proposed ASHRAE Standard 231P based on the presented work. Moreover, as-installed control sequences can be formally verified against the design specification, and a semantic model in Brick can be exported to aid in configuration of building analytics and fault detection. The paper presents what we believe is the first translation of a Modelica-implemented control sequence to a native implementation on a commercial control platform, using the webCTRL product line from Automated Logic. The paper also shows how a webCTRL implementation can be formally verified against its Modelica specification. These use cases have all been demonstrated with a prototype implementation that is now being further developed.

Wetter, Michael↗

Energy 101: Energy Efficient Commercial Buildings

Learn how commercial buildings can incorporate whole-building design to save energy and money while enhancing performance and comfort. This video highlights several energy-saving features of the Research Support Facility at the Energy Department's National Renewable Energy Laboratory-a model for high-performance office building design.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Commercial Integrated Heat Pump with Thermal Storage --Demonstrate Greater than 50% Average Annual Energy Savings, Compared with Baseline Heat Pump and Water Heater (Go/No-Go) FY16 4th Quarter Milestone Report

For this study, we authored a new air source integrated heat pump (AS-IHP) model in EnergyPlus, and conducted building energy simulations to demonstrate greater than 50% average energy savings, in comparison to a baseline heat pump with electric water heater, over 10 US cities, based on the EnergyPlus quick-service restaurant template building. We also assessed water heating energy saving potentials using ASIHP versus gas heating, and pointed out climate zones where AS-IHPs are promising.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

City and County Energy Profiles

The City and County Energy Profiles lookup table provides modeled electricity and natural gas consumption and expenditures, on-road vehicle fuel consumption, vehicle miles traveled, and associated emissions for each U.S. city and county. Please note this data is modeled and more precise data may be available from regional, state, or other sources. The modeling approach for electricity and natural gas is described in Sector-Specific Methodologies for Subnational Energy Modeling: https://www.nrel.gov/docs/fy19osti/72748.pdf. 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↗

Development of a Trip Energy Estimation Model Using Real-World Global Positioning System Driving Data: Preprint

A data-driven technique for estimation of energy requirements for a proposed vehicle trip has been developed. Based on over 700,000 miles of driving data, the technique has been applied to generate a model that estimates trip energy requirements. The model uses a novel binning approach to categorize driving by road type, traffic conditions, and driving profile. The trip-level energy estimations can easily be aggregated to any higher-level transportation system network desired. The model has been tested and validated on the Austin, Texas, data set used to build this model. Ground-truth energy consumption for the data set was obtained from Future Automotive Systems Technology Simulator (FASTSim) vehicle simulation results. The energy estimation model has demonstrated 12.1 percent normalized total absolute error. The energy estimation from the model can be used to inform control strategies in routing tools, such as change in departure time, alternate routing, and alternate destinations, to reduce energy consumption. The model can also be used to determine more accurate energy consumption of regional or national transportation networks if trip origin and destinations are known. Additionally, this method allows the estimation tool to be tuned to a specific driver or vehicle type.

33 ADVANCED PROPULSION SYSTEMS↗

Exploring Building Retrofit Strategies Using AutoBEM Under Future Weather Scenarios

This study evaluates the long-term effectiveness of energy conservation measures (ECMs) on building energy consumption using AutoBEM, a scalable modeling framework driven by the high-resolution Model America dataset. We simulated 18,951 buildings in Flagstaff, Arizona under four climate scenarios using Future Typical Meteorological Year (fTMY) weather files for six time periods spanning from 1980 to 2099. Six ECMs were analyzed across electricity and gas usage, including HVAC fuel-switching, insulation upgrades, and infiltration control. While some measures, such as reducing space infiltration by percentage, showed minimal or even negative impact on total energy savings at the aggregate level, they proved highly effective for specific building types. Conversely, HVAC electrification offers high gas reduction but shifts demand to electricity, highlighting critical trade-offs under different climate trajectories. Building-type-specific analysis under SSP5-RCP8.5 (2080–2099) revealed significant variation in ECM performance, underscoring the need for targeted retrofit strategies. This study demonstrates the power of combining fTMY projections with large-scale simulations to inform data-driven retrofit planning.

Chowdhury, Shovan [ORNL]↗

The Importance of Weather and Climate to Energy Systems: A Workshop on Next Generation Challenges in Energy–Climate Modeling

More than 80 international participants, representing weather, climate, and energy systems research, joined two 4-hour remote sessions to highlight and prioritize ongoing and future challenges in energy-climate modelling. The workshop had two primary goals: to build a deeper engagement across the “energy” and “climate” research communities, and to identify and begin to address the scientific challenges associated with modelling climate risk in energy systems.

17 WIND ENERGY↗

An economic and technical feasibility analysis of a dual-source heat pump using both the air and the ground

The study investigates the economic and technical performance of a novel dual-source heat pump (DSHP) compared with that of air-source heat pumps (ASHPs) and ground-source heat pumps (GSHPs). The DSHP can use both ambient air and the ground as a heat source or heat sink. It uses ambient air when its temperature is favorable for efficient heat pump operation. When the ambient temperature is too hot or cold, the ground source is used to retain high-efficiency heat pump operation. Since the DSHP can alternately use either the ground heat exchanger (GHE) or ambient air to meet the thermal load, the required size of GHE can be smaller than those of GSHPs. This study models the DSHP using a whole building energy simulation tool (EnergyPlus) coupled with a Python plug-in and Heat Pump Design Model (HPDM) to simulate its heating and cooling performance for a typical single-family home in 15 US climate zones. The required GHE size of the DSHP system is determined through simulations and compared with that of GSHPs. DSHP deployment can reduce electricity use compared to ASHPs, especially in cold climates where it shows a reduction of around 50%. When compared to GSHPs, DSHPs use 20%–40% more electricity in warm climates but consume around the same amount in moderate and colder climates. Since the DSHP can use air source when the ambient temperature is mild, the GHE size needed for the DSHP is about 40% less than that needed for GSHPs in hot climates and about 25% less in cold climates. In conclusion, the life cycle cost analysis shows that the DSHP is economically more feasible than ASHPs in colder regions and economically more feasible than GSHPs in hot and cold regions.

Dual-source heat pumps↗

MPCPy: An Open-Source Software Platform for Model Predictive Control in Buildings

Within the last decade, needs for building control systems that reduce cost, energy, or peak demand, and that facilitate building-grid integration, district-energy system optimization, and occupant interaction, while maintaining thermal comfort and indoor air quality, have come about. Current PID and schedule-based control systems are not capable of fulfilling these needs, while Model Predictive Control (MPC) could. Despite the critical role MPC-enabled buildings can play in future energy infrastructures, widespread adoption of MPC within the building industry has yet to occur. To address barriers associated with system setup and configuration, this paper introduces an open-source software platform that emphasizes use of self-tuning adaptive models, usability by non-experts of MPC, and a flexible architecture that enables application across projects.

Blum, David↗

Enabling Detailed Energy Analyses via the Technology Performance Exchange: Preprint

One of the key tenets to increasing adoption of energy efficiency solutions in the built environment is improving confidence in energy performance. Current industry practices make extensive use of predictive modeling, often via the use of sophisticated hourly or sub-hourly energy simulation programs, to account for site-specific parameters (e.g., climate zone, hours of operation, and space type) and arrive at a performance estimate. While such methods are highly precise, they invariably provide less than ideal accuracy due to a lack of high-quality, foundational energy performance input data. The Technology Performance Exchange was constructed to allow the transparent sharing of foundational, product-specific energy performance data, and leverages significant, external engineering efforts and a modular architecture to efficiently identify and codify the minimum information necessary to accurately predict product energy performance. This strongly-typed database resource represents a novel solution to a difficult and established problem. One of the most exciting benefits is the way in which the Technology Performance Exchange's application programming interface has been leveraged to integrate contributed foundational data into the Building Component Library. Via a series of scripts, data is automatically translated and parsed into the Building Component Library in a format that is immediately usable to the energy modeling community. This paper (1) presents a high-level overview of the project drivers and the structure of the Technology Performance Exchange; (2) offers a detailed examination of how technologies are incorporated and translated into powerful energy modeling code snippets; and (3) examines several benefits of this robust workflow.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Optimization control technology for building energy conservation

A simulation processor generates and stores a simulation model based on conditions associated with a physical structure, such as a building. A neural network processor implements a neural network, having an input layer coupled to receive sensor data from the structure and having an output layer coupled to supply control signals to the at least one electrically operable environmental control device. The neural network is trained using the simulation model. A particle swarm optimization processor programmed to receive the simulation results and perform particle swarm optimization, ascertains optimal parameters for controlling the at least one electrically operable environmental control device and supplies these optimal parameters to the neural network processor. The neural network processor uses the optimal parameters supplied by the particle swarm optimization processor to further train the neural network.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

National Cost-Effectiveness of ANSI/ASHRAE/IES Standard 90.1–2019

The purpose of this analysis is to examine the cost-effectiveness of the 2019 edition of ANSI/ASHRAE/IES Standard 90.1, which is developed by the ASHRAE Standard Standing Project Committee (SSPC) 90.1, and is the model energy standard for all commercial buildings and multifamily residential buildings over three floors. PNNL analyzed the cost-effectiveness of changes in Standard 90.1-2019, compared to the previous 90.1-2016 edition, as applied in commercial buildings across the United States. In reviewing proposed changes to Standard 90.1, the SSPC considers the cost-effectiveness of individual changes (addenda). Due to the continuous nature of the development process, however, ASHRAE does not evaluate the entire package of addenda from one edition of the standard to the next, which is of particular interest to adopting state and local governments. Providing states with an analysis of cost-effectiveness facilitates a more comprehensive understanding of the impacts associated with updated model energy codes, informs the state decision-making process and its authorities, and ultimately encourages greater adoption of updated of energy codes. This information also informs the development of future editions of Standard 90.1.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Device-level plug load disaggregation in a zero energy office building and opportunities for energy savings

Along with heating, cooling, ventilation, and lighting, plug loads are one of the principal consumers of energy in commercial buildings. Managing ever-changing plug loads is a great challenge given the quantity and variety of devices in commercial buildings and the cost of monitoring equipment. To address this issue, we propose a method for developing a disaggregated model of an office building's plug loads that utilizes power data from a small portion of monitored devices and a device inventory. Using data from metering and control devices in the Research Support Facility at the National Renewable Energy Laboratory, we compared our model to the building's plug load submeters. We found that the model was effective in predicting the shape of the building's average plug loads; however, it did not account for the entire magnitude of the load. With the disaggregated breakdown, we identified devices that contributed significantly to the building's unoccupied load, such as the audio visual equipment as well as devices that contributed significantly at specific times of the day, such as the microwaves at noon. This disaggregated information enables building owners to make more informed decisions with respect to plug load controls and energy efficiency upgrades. Furthermore, we highlight how the plug loads in the Research Support Facility have changed over time and offer recommendations for implementing this disaggregation method in other buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

New Whole-House Solutions Case Study: Low-Cost Evaluation of Energy Savings at the Community Scale - Fresno, California

In this project, IBACOS partnered with builder Wathen-Castanos Hybrid Homes to develop a simple and low-cost methodology by which community-scale energy savings can be evaluated based on results at the occupied test house level.Research focused on the builder and trade implementation of a whole-house systems integrated measures package and the actual utility usage in the houses at the community scale of production. Five occupants participated in this community-scale research by providing utility bills and information on occupancy and miscellaneous gas and electric appliance use for their houses. IBACOS used these utility data and background information to analyze the actual energy performance of the houses. Verification with measured data is an important component in predictive energy modeling. The actual utility bill readings were compared to projected energy consumption using BEopt with actual weather and thermostat set points for normalization.

Building America↗

Boston Architectural College Urban Sustainability Initiative

The Boston Architectural College's Urban Sustainability initiative is a demonstration project as defined by the National Energy Technology Laboratory. BAC's proposed project with the U.S. Department of Energy - NETL, is a large part of that overall initiative. The BAC's Urban Sustainability Initiative is a multi-part project with several important goals and objectives that will have a significant impact on the surrounding neighborhood including: energy conservation, reduction of storm water runoff, generation of power through alternative energy sources, elimination/reduction of BAC carbon footprint, and to create a vehicle for ongoing public outreach and education. Education and outreach opportunities will serve to add to the already comprehensive Sustainability Design courses offered at BAC relative to energy savings, performance and conservation in building design. At the finish of these essential capital projects there will be technical materials created for the education of the design, sustainability, engineering, community development and historic preservation communities, to inform a new generation of environmentally-minded designers and practitioners, the city of Boston and the general public. The purpose of the initiative, through our green renovations program, is to develop our green alley projects and energy saving renovations to the BAC physical plant, to serve as a working model for energy efficient design in enclosed 19th century and 20th century urban sites and as an educational laboratory for teaching ecological and sustainable technologies to students and the public while creating jobs. The scope of our project as it relates to the BAC and the U.S. Department of Energy- NETL combined efforts includes: Task I of the project is Phase II (Green Alley). Task I encompasses various renovation activities that will demonstrate the effectiveness of permeable paving and ground water recharge systems. It will aid in the reduction of storm water runoff into the Charles River Basin in one of its most significantly polluted sections and, will provide a green renovation mechanism for the redirected storm water of a public alley way. This activity is designed to improve the quality of water recharging the ground water and protecting the vulnerable wood pilings under many of the historic masonry buildings in Boston's Back Bay. Sustainable design research and system monitoring opportunities will also be incorporated, providing ongoing tools for public outreach and education through innovative signage and "virtual tour" technology. The monitoring will include a "building performance dash board" that reflects real time operating conditions and improvements in environmental and economic performance to be prominently displayed on the face of our 320 Newbury Street building (approximately 1.5 million people walk by annually). The project site and demonstration area is located at the rear of 951 Boylston Street, Boston, MA 02115 and the parking area adjacent to Public Alley #444 in Boston's historic Back Bay. Task II of the project is Geothermal Solution. This task involves the installation of approximately seven Geothermal wells which will tap into the earth's constant underground temperatures to provide air-conditioning and heating for BAC facilities. The environmentally friendly geothermal system uses no fossil fuel, produces no emissions and runs silently, providing a sustainable model for commercial and residential buildings throughout Boston. Ultimately the combination of this project and other projects will assist in making the BAC "carbon-neutral", and could generate enough additional energy to provide free power to the Engine 33 and Ladder 15 Firehouse located at 941 Boylston Street. The project is located at the rear of 951 Boylston Street, Boston, MA 02115 and the parking area adjacent to Public Alley #444 in Boston's historic Back Bay. Task III of the project is the Sustainability Design Curriculum at the BAC. The BAC is the nation’s largest independent, multi-disciplinary college of spatial design, and a leader in sustainable design education. The Sustainabiltiy Design program, in particular, focuses on energy efficiency,energy performance, energy modeling, energy and air quality principles, green building, renewable energy alternatives and much more. Additionally, the Urban Sustainability Initiative has an objective of providing courses relative to the BAC's demonstration project with DOE, the project’s period of performance activities, subsequent performance data and anticipated sustainability teaching tools. In keeping with BAC's practice based curriculum, students have been involved in the discussion and planning of this project since its inception and sustainability issues have become a part of other programs throughout the college as well. Students will continue to benefit from this hands-on learning opportunity. The project is located at 320 Newbury Street Boston, MA 02115. Task IV of the project is Program Management and Reporting. This task will involve BAC's oversight and general management of the entire project including subcontract administration, contracts administration, technical and financial reporting, design and renovation assistance, other deliverables in accordance with the Federal Assistance reporting Checklist and other contractual obligations and provisions.

15 GEOTHERMAL ENERGY↗

Building thermal load prediction through shallow machine learning and deep learning

Building thermal load prediction informs the optimization of cooling plant and thermal energy storage. Physics-based prediction models of building thermal load are constrained by the model and input complexity. In this study, we developed 12 data-driven models (7 shallow learning, 2 deep learning, and 3 heuristic methods) to predict building thermal load and compared shallow machine learning and deep learning. The 12 prediction models were compared with the measured cooling demand. It was found XGBoost (Extreme Gradient Boost) and LSTM (Long Short Term Memory) provided the most accurate load prediction in the shallow and deep learning category, and both outperformed the best baseline model, which uses the previous day's data for prediction. Then, we discussed how the prediction horizon and input uncertainty would influence the load prediction accuracy. Major conclusions are twofold: first, LSTM performs well in short-term prediction (1 h ahead) but not in long term prediction (24 h ahead), because the sequential information becomes less relevant and accordingly not so useful when the prediction horizon is long. Second, the presence of weather forecast uncertainty deteriorates XGBoost's accuracy and favors LSTM, because the sequential information makes the model more robust to input uncertainty. Training the model with the uncertain rather than accurate weather data could enhance the model's robustness. Finally, our findings have two implications for practice. First, LSTM is recommended for short-term load prediction given that weather forecast uncertainty is unavoidable. Second, XGBoost is recommended for long term prediction, and the model should be trained with the presence of input uncertainty.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗