Institutional heat wave analysis by building energy modeling fleet and meter data
Not Available
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
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Results for “energy models”
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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Integration of Electric Vehicle Charging Loads in Residential Building Stock Energy Modeling
The rapid adoption of electric vehicles (EVs) has resulted in significant new household electric loads that have the potential to change how energy costs are incurred by homeowners and the landscape of utility operations and energy infrastructure. Whereas adoption patterns and magnitudes of residential building and EV charging loads are influenced by distinct factors, the loads themselves are tightly coupled with the behavior of the individual occupants and EV owners.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Building Energy Modeling Enhancements to Identify Least-Cost Pathways to Net-Zero Carbon Homes: Preprint
Residential grid-interactive efficient buildings (GEBs) can utilize high levels of energy efficiency and demand flexibility to deliver value to occupants, the grid, and society. However, without the ability to analyze, design, and optimize residential GEBs there is a risk that homes will not realize their full potential value as efficient and dynamic resources, which could ultimately lead to higher than necessary energy costs for U.S. households and lower realization of potential energy efficiency and demand flexibility benefits such as increased convenience/automation, thermal comfort, durability, indoor air quality, and resilience. In 2018, the National Renewable Energy Laboratory (NREL) Residential Modeling Team developed a vision for an open source Residential GEB Analytics Platform built within DOE's EnergyPlus and OpenStudio modeling environment that would enable the design and optimization of residential GEBs, including the identification of least-cost pathways to highly energy-efficient and energy-flexible homes (e.g., net zero carbon homes). We created a detailed workplan for the development of new and enhanced residential GEB component-level models for EnergyPlus and OpenStudio needed to progress toward the vision for the analytics platform while delivering near-term benefits to industry. This paper 1) presents the vision for the platform, summarizing the workplan for residential GEB modeling enhancements; 2) highlights significant advancements that have been achieved between 2018 and 2022, including stochastic residential occupancy modeling, flexible water heater modeling, detailed lithium-ion stationary battery modeling, and realistic residential HVAC modeling; and 3) outlines ongoing efforts and next steps toward the full vision for the platform.
building energy modeling↗
Modeling energy flow and nutrient cycling in natural semiarid grassland ecosystems with the aid of thematic mapper data
Energy flow and nutrient cycling were modeled as affected by herbivory on selected intensive sites along gradients of precipitation and soils, validating the model output by monitoring selected parameters with data derived from the Thematic Mapper (TM). Herbivore production was modeled along the gradient of soils and herbivory, and validated with data derived from TM in a spatial data base.
Lewis, James K.↗
A data schema for exchanging information between urban building energy models and urban microclimate models in coupled simulations
Not Available
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
From Safer Space Travel to Improved Fisheries Monitoring: Wave Energy Modeling Tool Helps Design Tomorrow's Tech
What does wave energy have to do with space exploration? It turns out, plenty. The National Aeronautics and Space Administration (NASA) is working toward launching the Artemis I mission, an uncrewed lunar test flight, in 2021. It will be the maiden flight of the Space Launch System heavy-lift launch vehicle, as well as the Orion crew module (CM). Orion will orbit the moon for multiple days before returning to Earth and landing in the Pacific Ocean.
Tannen S Vanzwieten↗
Identification of Climatological Representative Days in the Mid-Atlantic for High-Fidelity Offshore Wind Energy Modeling
The goal of reaching 30 GW of offshore wind energy by 2030 becomes more realistic with the continued approval of offshore wind energy areas by the Biden Administration. In the Mid-Atlantic, where wind energy projects are in the most advanced stages of development, there is increased research focus on the eventual interaction of these wind farms. These interactions, in the form of wakes and cluster wakes, or wakes from multiple wind farms, could have detrimental effects on power production and forecastability for downwind wind farms (Pryor et al. 2022, Golbazi et al. 2022, Rosencrans et al. 2023). To help alleviate these issues, numerical simulations in the form of numerical weather prediction (NWP) and large eddy simulations (LES) can provide insight into when cluster wake situations may occur, but running such simulations can be expensive and difficult to run for multiple years. In this study, we leverage and build upon existing techniques in the literature (Fischereit et al. 2022) to identify climatologically representative days for wind energy areas in the Mid-Atlantic where conditions would promote cluster wake situations. We select meteorological variables (wind speed, wind direction, atmospheric stability, boundary-layer height, TKE) critical to understanding wind energy production and wake propagation. We then consider two different NWP datasets of varying spatial and temporal resolution: ERA5 provides data at hourly intervals from 1940 to present at 0.25 deg (31 km) spatial resolution (Hersbach et al. 2020), and the NOW-23 dataset provides data at 5-minute resolution for 21 years at 2-km spatial resolution (Bodini et al. 2020). Our first step is to compare these two datasets for an overlapping 21-year time period. Initial results show that the required number of days to represent the long-term climate increases with each additional variable considered. In their study of the German Bight, Fischereit et al. (2022) found that they could represent the long-term wind and wave climate in a "near-perfect" way with -180 days, by reaching a Perkins Skill Score (PSS) of 0.9; our investigation of the mid-Atlantic wind resource region with ERA5 and NOW-23 data suggests that we will need -100 days to reach a PSS of 0.9. As we expand our parameter space to include multiple variables, the number of required days will likely grow. These results will ultimately be used to select case studies to best represent cluster wake conditions that apply to this region for the lifetime of likely wind farms in this mid-Atlantic region.
clusterwakes↗
A High-Granularity Approach to Modeling Energy Consumption and Savings Potential in the U.S. Residential Building Stock: Preprint
Building simulations are increasingly used in various applications related to energy efficient buildings. For individual buildings, applications include: design of new buildings, prediction of retrofit savings, ratings, performance path code compliance and qualification for incentives. Beyond individual building applications, larger scale applications (across the stock of buildings at various scales: national, regional and state) include: codes and standards development, utility program design, regional/state planning, and technology assessments. For these sorts of applications, a set of representative buildings are typically simulated to predict performance of the entire population of buildings. Focusing on the U.S. single-family residential building stock, this paper will describe how multiple data sources for building characteristics are combined into a highly-granular database that preserves the important interdependencies of the characteristics. We will present the sampling technique used to generate a representative set of thousands (up to hundreds of thousands) of building models. We will also present results of detailed calibrations against building stock consumption data.
building stock↗
Machine learning assisted derivation of minimal low-energy models for metallic magnets
Abstract We consider the problem of extracting a low-energy spin Hamiltonian from a triangular Kondo Lattice Model (KLM). The non-analytic dependence of the effective spin-spin interactions on the Kondo exchange excludes the use of perturbation theory beyond the second order. We then introduce a Machine Learning (ML) assisted protocol to extract effective two- and four-spin interactions. The resulting spin model reproduces the phase diagram of the original KLM as a function of magnetic field and single-ion anisotropy and reveals the effective four-spin interactions that stabilize the field-induced skyrmion crystal phase. Moreover, this model enables the computation of static and dynamical properties with a much lower numerical cost relative to the original KLM. A comparison of the dynamical spin structure factor in the fully polarized phase computed with both models reveals a good agreement for the magnon dispersion even though this information was not included in the training data set.
Sharma, Vikram (ORCID:0000000189105519)↗
URBANopt: An Open-Source Software Development Kit for Community and Urban District Energy Modeling: Preprint
Urban building modeling tools are developing rapidly; these tools use emerging simulation workflows for specific urban environmental design tasks, such as assessing the impacts of energy efficiency technologies at a district scale. However, with the emergence of new environmental design tasks, addressing all possible use cases and tasks is challenging and cannot be covered by a single tool. Urban-scale analysis at this level of complexity often requires linking multiple emerging tools, rather than using a single tool, to adequately evaluate a variety of possible fields in urban environmental design. To achieve this, flexible platforms are needed to support multiple input formats (e.g., geometric and non-geometric building properties), enabling the mapping of such inputs to underlying simulation engines. This paper provides an overview of the open-source URBANopt Software Development Kit (SDK) for modeling high-performance buildings and energy systems at a district scale. URBANopt's flexible SDK is composed of several modules that can be customized to integrate with other tools and generate new workflows to perform urban environmental design tasks, such as capturing interactions between individual buildings, district energy systems, distributed energy resources, and the electric distribution grid. We describe the functionality of the core SDK modules in URBANopt (called Core Gem, GeoJSON Gem, and Scenario Gem) and discuss the flexibility of these modules as a means of integration with a variety of tools. We also document and demonstrate technical details of writing and combining new modules to create customized workflows. Finally, we present a case study that uses the URBANopt SDK to model a hypothetical mixed-use urban project and simulate various scenarios to meet district energy performance goals.
buildings↗
Heating and Cooling Energy Modeling of 3D-Printed Concrete Construction of Residential Buildings [Slides]
3D printed concrete construction (C3DP) is an emerging technology that comes with the associated benefits of high thermal mass walls. We investigated using BEopt software the heating and cooling energy use of a single story C3DP-constructed house. We then compared the heating/cooling energy use of the C3DP house against the corresponding energy use in a traditional timber (wood) frame construction (WFC), as well as a concrete masonry unit (CMU) construction. Both peak energy use for heating and cooling (Btu), as well as base energy use for both heating and cooling (MMBtu/yr) of the C3DP design were compared against the WFC and CMU baseline construction of identical geometries and orientations across all eight climate zones defined in the International Energy Conservation Code (IECC). The BEopt models for all three constructions (C3DP, WFC, and CMU) were built to comply with the 2018 IECC code. Results indicate that C3DP construction has significant heating and cooling energy benefits in certain climate zones, with the highest peak cooling energy savings (9% compared to WFC, and 5% compared to CMU) in the IECC Climate Zone 1 in the month of July. The peak heating and cooling energy demand reduction of C3DP were found to be more significant than the base heating and cooling energy demand in all IECC climate zones. The 2018 IECC compliance-related U-Factor adjustments of all models also resulted in more peak energy savings of the C3DP design in the cooling-dominated climate zones (climate zones 1 and 2), moderate energy savings in moderate climate zones (climate zones 3, 4, and 6), and little to no change in energy savings in very cold climates (climate zones 7 and 8).
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Exploring Grid-Interactive Efficient Building Strategies for Laboratories Through Energy Modeling
Laboratories are often overlooked in demand flexibility research due to constraints on their operations as mission critical facilities, despite the major role they play in an organization's emissions. Laboratories consume 3-4 times more energy than a typical office building and are commonly the largest energy users on any campus. Consequently, most laboratories in the United States are significant contributors to their organization's carbon footprint if their energy needs are met through the combustion of fossil fuels. As part of the initiative to decarbonize laboratories, this report documents an analysis on specifically grid-interactive efficient building (GEB) opportunities for reducing energy costs and emissions associated with laboratory operations. The goal of this initiative was to provide a case study and guidance on how to use OpenStudio and REopt as modeling tools for GEB technologies and strategies in laboratory environments across different climate zones in the United States. The analysis found that efficiency-based GEB strategies had the most significant impact on laboratory operations, while load-shedding and load-shifting GEB strategies produced smaller results. The culmination of these approaches applied across all five climate zones generated on average: 1) 28% energy cost savings and 30% greenhouse gas (GHG) emissions reductions, and 2) 4% enhanced energy cost savings under a time-of-use (TOU) pricing schedule compared to traditional pricing schemes. Grid-interactive efficiency building measures were found to produce the greatest energy savings in both electricity and natural gas, particularly in regions with high electrical loads, such as warm climates for cooling. Laboratories that had high levels of natural gas consumption, meanwhile, experienced the greatest emission reductions. The report concludes with an analysis on the opportunities for flexible loads in lab spaces and how small-scale measures in addition to opaque pricing structures for peak demand could become barriers to demand flexibility planning. The report also explores how electrifying laboratory buildings with heat pumps could reduce energy costs and GHG emissions.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Informing electrification strategies of residential neighborhoods with urban building energy modeling
Electrifying end uses is a key strategy to reducing GHG emissions in buildings. However, it may increase peak electricity demand that triggers the need to upgrade the existing power distribution system, leading to delays in electrification and needs of significant investment. There is also concern that building electrification may cause an increase of energy costs, leading to further energy burden for low-income communities. This study uses the urban scale building modeling tool CityBES to assess the electrification impacts of more than 43,000 residential buildings in a neighborhood of Portland, Oregon, USA. Energy efficiency upgrades were investigated on their potential to mitigate the increase of peak electricity demand and energy burden. Simulation results from the calibrated EnergyPlus models show that electrification with heat pumps for space heating and cooling as well as for domestic water heating can reduce CO2e emissions by 38%, but increase peak electricity demand by about 9% from the baseline building stock. Combining electrification measures and energy efficiency upgrades can reduce CO2e emissions by 48% while reducing peak electricity demand by 6% and saving the median household energy costs by 28%. City and utility decision makers should consider integrating energy efficiency upgrades with electrification measures as an effective residential building electrification strategy, which significantly reduces carbon emissions, caps or even decreases peak demand while reducing energy burden of residents.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Obstruction and Interference in Low-Energy Models for Twisted Bilayer Graphene
The electronic bands of twisted bilayer graphene (TBLG) with a large-period moiré superlattice fracture to form narrow Bloch minibands that are spectrally isolated by forbidden energy gaps from remote dispersive bands. When these gaps are sufficiently large, one can study a band-projected Hamiltonian that correctly represents the dynamics within the minibands. This inevitably introduces nontrivial geometrical constraints that arise from the assumed form of the projection. Here we show that this choice has a profound consequence in a low-energy experimentally observable signature that therefore can be used to tightly constrain the analytic form of the appropriate low-energy theory. Additionally, we find that this can be accomplished by a careful analysis of the electron density produced by backscattering of Bloch waves from an impurity potential localized on the moiré superlattice scale. We provide numerical estimates of the effect that can guide experimental work to clearly discriminate between competing models for the low-energy band structure.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Net Zero Energy Model for Wastewater Treatment Plants
The primary objective of this study is to achieve net-zero energy (NZE) wastewater treatment plants (WWTPs) by utilizing energy efficiency opportunities (EEO), combined heat and power (CHP) systems, and other renewable energy (RE) sources, e.g., solar, water, and wind powers. Herein, this study discusses an innovative energy solution for WWTPs in the United States, and one of the WWTPs with a flow capacity of 1.5 million gallons per day (MGD) was selected as a case study. An optimization tool, Hybrid Optimization of Multiple Energy Resources (HOMER) software, is used in this study to find the best energy system configuration to run the system. An energy audit for one WWTP was conducted in early 2020 and the report is used to do this study. The proposed EEOs were able to reduce WWTP energy consumption by about 11%. The excess anaerobic digester gas was utilized in a CHP system to cover about 42% of the facility’s consumption. Also, 3% of the utility energy consumption can be claimed by microturbines in the aeration tanks. Another two renewable energy systems, solar photovoltaic (PV) with 29% and water turbines with 15%, contribute to covering 100% of the WWTP energy consumption and achieving an NZE WWTP.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
An energy model of droplet impingement on an inclined wall under isothermal and non-isothermal environments
The observation of spray-wall interaction is of great importance to understand the dynamics that occur during fuel impingement onto the chamber wall or piston surfaces in internal combustion engines. It is found that the maximum spreading length of an impinged droplet can provide a quantitative estimation of heat transfer and energy transformation for spray-wall interaction. Furthermore, it influences the air-fuel mixing and hydrocarbon and particle emissions at combusting conditions. In this paper, an analytical model of different droplet-wall impingement conditions is developed in terms of βm (dimensionless maximum spreading length, the ratio of maximum spreading length to initial droplet diameter) to understand the detailed impinging dynamic process. These conditions are grouped as: a single diesel droplet impinging on the wall with different inclined angles (α); cold wall - heated droplet and heated wall - cold droplet impingement when inclined angle of the wall is 0°, respectively. The analytical model is built up based on the energy conservation that considers kinetic energy, gravitation energy, and surface energy before impingement, as well as viscous dissipation, gravitation energy, adhesion energy, deformation energy, and heat energy after impingement. The experimental work of diesel droplet impinging on an inclined wall is performed at a certain range of the Weber number (We of 33 to 420) with various inclined angles (α of 0° to 45°), while for inclined angle is 0°, droplet and wall temperature are varied from 25°C to 150°C to study the effects of the inclined angle and temperature on the temporal evolution of the post-impingement characteristics (i.e. droplet spreading length, dynamic contact angle). The analytical model is validated and evaluated at the aforementioned experimental operating points. The validated model can be employed to predict maximum spreading length of the droplet impinged on the wall. It is further utilized to determine the transition from capillary regime to kinetic regime, then to viscous regime at different inclined angle of the wall.
42 ENGINEERING↗
Towards Trust-Augmented Visual Analytics for Data-Driven Energy Modeling
The promise of data-driven predictive modeling is being increasingly realized in various science and engineering disciplines, where experts are used to the more conventional, simulation-driven modeling practices. However, trust remains a bottleneck for greater adoption of machine learning-based models for domain experts, who might not be necessarily trained in data science. In this paper, we focus on the building energy domain, where physics-based simulations are being complemented or replaced by machine learning-based methods for forecasting energy supply and demand at various spatio-temporal scales. We study the trust problem in close collaboration with energy scientists and engineers and describe how visual analytics can be leveraged for alleviating this trust bottleneck for stakeholders with varying degrees of expertise and analytics goals in this domain.
Kandakatla, Akshith R.↗