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At least 217 records · Page 12

Biases to primordial non-Gaussianity measurements from CMB secondary anisotropies

ABSTRACT Our view of the last-scattering surface in the cosmic microwave background (CMB) is obscured by secondary anisotropies, sourced by scattering, extragalactic emission, and gravitational processes between recombination and observation. Whilst it is established that non-Gaussianity from the correlation between the integrated-Sachs–Wolfe (ISW) effect and gravitational lensing can significantly bias primordial non-Gaussianity (PNG) searches, recent work by Hill suggests that other combinations of secondary anisotropies can also produce significant biases. Building on that work, we use the WebSky and Sehgal et al. simulations to perform an extensive examination of possible biases to PNG measurements for the local, equilateral and orthogonal shapes. For a Planck-like CMB experiment, without foreground cleaning, we find significant biases from cosmic infrared background (CIB)-lensing and thermal Sunyaev–Zel’dovich (tSZ)-lensing bispectra for the local and orthogonal templates, and from CIB and tSZ bispectra for the equilateral template. For future experiments, such as the Simons Observatory, biases from correlations between the ISW effect and the tSZ and CIB will also become important. Finally, we investigate the effectiveness of foreground-cleaning techniques to suppress these biases. We find that the majority of these biases are effectively suppressed by the internal-linear combination method with a total bias below the $1\, \sigma$ statistical error for both experiments. However, the small total bias arises from the cancellation of several $1\, \sigma$ biases for Planck-like experiments and $2\, \sigma$ biases for SO-like. As this cancellation is likely sensitive to the modelling, to ensure robustness against these biases, we recommend that explicit removal methods should be used.

Astronomy & Astrophysics↗

Cold Climate Integrated Heat Pump with Energy Storage for Grid-Responsive Control

An air-source integrated heat pump (ASIHP) is a multifunctional unit, capable of space cooling, space heating, and water heating. We developed a packaged cold climate ASIHP, using a multi-stage compressor, capable of working down to -25°C, and providing 100% rated capacity down to -15°C with a heating COP > 2.2. An innovative system configuration and related controls were developed to solve charge balance in the integrated heat pump, smooth charge migration, and mode transition among multiple working modes. Extensive laboratory investigations for individual modes were performed to verify the performance. Its integrated heating capability provides speedy water heating to meet domestic hot water use and store heating energy in a phase change material ceiling/panel. The heating energy storage can be used to shift the load when the electricity price is high. Based on the performance data and a calibrated model from the laboratory testing, we conducted building energy simulations driven by a model predictive control, using EnergyPlus in one U.S. cold climate zone to investigate the grid-responsive control strategy and estimate the utility cost reduction potential.

Shen, Bo↗

Towards Learning-Based Architectures for Sensor Impact Evaluation in Building Controls

Advanced control algorithms for building systems are known to have significant potential in reducing energy consumption while optimizing thermal comfort. The success of such algorithms is critically contingent on several different types of sensor systems, which are in turn, used for continuous monitoring, identification and estimation of several important building states, such as temperatures, humidity, air quality, power consumption and occupancy status. Nonidealities in any of these sensors can lead to significant performance degradation of the control functionalities, and may lead to unwanted sub-optimal building operation. In this paper, we provide a simulation example with a high-fidelity building model, for a particular use-case of advanced optimization-based control in buildings, i.e., occupancy-based controls. We show how imperfections in occupancy sensing can offset performance. Subsequently, we discuss a novel learning-based architecture to efficiently evaluate the impact of sensor nonidealities for building systems, in context of advanced control algorithms.

Bhattacharya, Saptarshi↗

Quantifying and Optimizing the Energy Benefits of Mass Timber Construction

The International Mass Timber Alliance (IMTA) is a global organization of industry leaders, engineers, scientists, and associations dedicated to advancing mass timber construction. Its mission is to generate and disseminate scientific data supporting the development of standardized construction and energy efficient practices that promote the adoption of mass timber worldwide. IMTA collaborated with Oak Ridge National Laboratory (ORNL) to leverage ORNL’s expertise in building envelope modeling and testing to evaluate how mass timber construction can reduce peak heating and cooling demand, lower overall energy use, and improve resilience during power outages. A previous study of 80 mass timber buildings in Finland found measured energy use up to 50% lower than predicted by simulation. This project aimed to validate and extend those findings for U.S. buildings through analytical modeling, laboratory testing, and full-scale building evaluations. The research focused on the thermal performance of low-embodied-energy wall assemblies, such as cross-laminated timber (CLT) panels and log walls, with particular attention to the effects of thermal inertia on indoor comfort and energy performance. While mass timber’s structural and fire-resistance properties are well documented, its whole-building thermal behavior has received limited attention. Field data, simulation results, and resilience testing from this study will inform future modeling practices, design guidelines, and construction practices by quantifying the unique thermal and demand-flexibility benefits of mass timber construction.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

BETO 2021 Peer Review - Inverse Bioproduct Design Through Machine Learning and Molecular Simulation

This work aims to identify performance advantaged bioproducts (PABPs) through property prediction, which will guide experimental synthesis. The impact of this work will be faster market adoption of bioproducts with greater performance relative to incumbent products. We have identified >106 bioproduct candidates, but only some will have superior performance to create a market pull. High-throughput property prediction, enabled by machine learning, and elucidation of structure-function relationships, enabled by molecular simulation, provide a hypothesis driven approach for down selection of candidate biomolecules to pursue experimentally. To enable machine learning and molecular simulation for bioproduct discovery, automated structure generation and embedding must capture relevant features for prediction, databases must cover domains applicable to biobased products, and best practices for simulation of polymer systems must be developed. To address these challenges, we have established bioproduct relevant datasets, developed high-throughput polymer structure generation, and built end-to-end neural networks that have predicted 8 properties for >1.4 x 106 biopolymers. A molecular simulation pipeline for building, running, and analyzing polymers and polymer additives is being used to predict performance and develop design principles of biobased products. In collaboration with the PABP synthesis project, these computational tools are guiding synthesis and informing design of PABPs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

A Simplified Building Modeling Approach for Identifying Whole Building Retrofits

Building Energy Modeling (BEM) is an effective strategy for optimizing new and retrofit building design, evaluating its energy savings potential and rating its energy performance. However, because of the complexity and expense of modeling, only a small percentage of buildings are simulated. Over 80% of buildings are <25,000 ft2, and the cost associated with BEM can be a big deterrent for reaching this subsector, which usually ends up complying with energy codes and qualifying for incentives using prescriptive measures or generalized design guides. Similarly, efficiency considerations for small building retrofits are often identified through deemed measures or Technical Reference Manuals, which though a scalable and convenient approach, can limit innovation in design and optimization of measures. Simplification of the modeling process, where appropriate, can lead to increased energy savings and more informed decision making. This paper will discuss the development of a ruleset for a simplified approach based on the Performance Rating Method (PRM) contained in ASHRAE Standard 90.1. The approach aims to simplify and lower the cost of the energy modeling process in a controlled and documented manner. An anticipated outcome is an increased use of BEM and its effectiveness in the design, retrofit and operation of commercial buildings. In addition, simplification of the modeling process will result in fewer errors from misinterpretations of both program requirements and program intent in simulation software. The paper will discuss the need and advantages of a simplified modeling approach for whole building retrofits and the technical process for achieving the same.

Goel, Supriya↗

Modeled Results of Four Residential Energy Efficiency Measure Packages for Deriving Advanced Building Construction Research Targets

The Advanced Building Construction (ABC) Initiative from the U.S. Department of Energy Building Technologies Office is working to accelerate industrialized construction innovations for decarbonizing buildings. To inform performance and cost targets for research under the ABC Initiative, this analysis used the ResStock™ tool to evaluate the energy savings, utility bill impacts, and carbon emissions impacts of four simulated upgrade packages with specific target performance levels on a large sample of residential dwelling units (approximately 550,000) representative of the U.S. housing stock.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Model-based data center cooling controls comparative co-design

This article presents a comparative simulation-based control logic design process. It uses the Control Description Language (CDL) and the ASHRAE Guideline 36 high-performing building control sequences with the Modelica Buildings Library (MBL) to demonstrate a comparative analysis of two control designs for a data center chilled water plant. Details include a description of the closed-loop plant and control design methodology, including sizing and parameterization, base and alternative (Guideline 36) control logic with software implementation structure, and outline of the simulation experimentation process. The selected control designs are paired with comparable chilled water plant configurations. The models include a chiller, a water-side economizer, and an evaporative cooling tower. The plant provides cooling at 27ºC zone supply air temperature to a data center in Sacramento, CA. The comparative simulation results examined the impacts of a selected control logic detail, and present an example model-based design application. Overall, the simulation results showed a 25% annual and a 18% summer energy use reduction for alternative controls. This shows that simulation-based control logic design performance evaluation can improve energy efficiency and resilience aspects of system controls at large.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Performance Assessment of the Overall Building Envelope Thermal Performance—Building Envelope Performance (BEP) Metric

Today, to describe the thermal performance of the building envelope and its components we use a variation of metrics; such as, R-value, ACH (air exchange rate per hour), SHGC (solar heat gain coefficient) of windows, U-factor etc. None of these performance indicators is meant to represent the overall thermal performance. In this paper, such a metric is introduced, the BEP (building envelope performance) value. Unlike the thermal resistance, typically expressed as an R-value, the BEP-value considers additional elements of heat transfer that affect the energy demand of the building because of exterior and interior (solar) thermal loads: conductive and radiant heat transfer, and air infiltration. To demonstrate BEP’s utility, validation studies were carried out by comparing the BEP-value to theoretical results using whole building energy simulation tools such as EnergyPlus and WUFI Plus. Results show that BEP calculations are comparable to calculations made using these simulation tools and that unlike other similar metrics, the BEP-value accounts for all heat transfer mechanisms that are relevant for the overall energy performance of the building envelope. The BEP-value thus allows comparing envelopes of buildings with different use types in a fair and realistic manner.

42 ENGINEERING↗

Ecosystem-Level Biomimicry for the Built Environment: Adopting Systems Ecology Principles for the Control of Heterogeneous Energy Systems

This paper presents, to our knowledge, the first system-level engineering study to bio-mimic the cybernetics and flow dynamics of energy resources in natural ecosystems for the control of heterogeneous energy infrastructures in the built environment. To this end, we introduce a novel Biomimetic Pulsing State (BPS) control that functionally mimics mature ecosystems. A preliminary Modelica-based case study features a single-family residential building with electrical and HVAC subsystems. The BPS control objective is to minimize the energy exchange between the building and the grid for the purposes of future self-supporting buildings and grid stability. The building contains PV, a wind turbine, a battery storage system, and a fan coil/heat pump HVAC system served by an ambient district energy network. Evaluating the control performance (BPS vs. constant setpoint) over several renewable energy scenarios (net importer, net zero, net exporter), simulation results show how the building's HVAC system can dynamically adjust its electrical load and temperatures to the electrical system's net energy status in real-time with BPS control. As a net importer, the heat pump consumed 29% less energy and its peak power reduced by 15% with BPS control compared to the constant setpoint case, with the zone air temperature 1 degree C lower on average. As a net exporter, the heat pump effectively consumed the same energy, but the peak power increased by 34% with BPS control, while the zone air temperature was 1 degree C higher when renewable energy was abundant, preheating the home. BPS and constant setpoint control produced comparable results under a net zero scenario. While further evaluation is essential, BPS control may help communities meet their sustainability and resiliency targets as they transition towards fully distributed and renewable energy grids.

biomimicry↗

Transforming Windows from Energy Liabilities to Zero-Energy Assets: Next-Generation Solutions for Buildings

Windows have traditionally contributed to a building's HVAC load, but they can also become a source of net energy gain or even operate as zero-energy components. For heating applications, highly insulating windows can harness more solar heat than the energy lost through them, transforming windows from energy liabilities to assets. Dynamic glazings provide further benefits by regulating solar heat gain, reducing cooling loads in summer and heating demands in winter. This simulation study focuses on developing the next generation of zero-energy windows (ZEW) for residential new construction. Through annual energy simulations across climate zones 1-8, ZEW performance benchmarks were established based on current code-level buildings, and we've identified the regions where meeting ZEW standards are most achievable. This work evaluates both static and dynamic window technologies, assessing their effects on annual energy use and cost. Key findings demonstrate that ZEW performance is achievable across diverse climate zones, with specific regional requirements. Most climate zones from 3-8 can achieve ZEW with specific configurations, while some warm climates (1-2) appear challenging for ZEW implementation. Climate zones 4-6 consistently allow for zero energy window implementation, offering multiple pathways through either static or dynamic window technologies. Colder climate zones (7-8) ZEW products allow for higher SHGC values while requiring low U-values.

Yu, Lili↗

CyDER: A Cyber Physical Co-simulation Platform for Distributed Energy Resources in Smartgrids

The CyDER project aimed at developing an open-source, modular and scalable co-simulation platform for power grids with large shares of Distributed Energy Resources (DERs). The project partners are the Lawrence Berkeley National Lab (LBNL), Lawrence Livermore National Lab (LLNL), PG&E, SolarCity, and ChargePoint. The prime recipient is LBNL; SolarCity and ChargePoint were partners for the project’s first two years. Increased DER integration introduces a number of challenges in power grid operation including a more dynamic interaction between the transmission grid and distribution grids, and increased modeling complexity. Although specialized software exists to precisely model different components of the power system, it is far from trivial to integrate all various models and perform a holistic simulation. Instead of replicating all models in a common simulation program, a commonly accepted approach to tackle this model diversity is to couple third-party simulators and models through a co-simulation platform that coordinates information exchange among the various components. Following this line of research, this project’s objective was to develop a co-simulation platform based on a widely accepted industrial standard called Functional Mockup Interface (FMI). Within this process, the project developed models compliant with the FMI standard, called Functional Mockup Units (FMUs), and used them to perform various operational and planning power system analyses. Relying and building upon an industrial standard is the main differentiation of this project compared with previous or parallel efforts in the co-simulation area. Particular emphasis was put on delivering software utilities to facilitate setting up and running co-simulations by end-users. Furthermore, a strong aspect of this project is demonstrating that co-simulation techniques can be used to perform Hardware-in-the-Loop (HIL) simulations that couple software components (e.g., simulated models) with hardware components (e.g., real devices such PV systems and batteries). The long-term goal of CyDER project is to help establish FMI as a powerful standard for co-simulation and promote adoption by electric utilities and other interested stakeholders. The main accomplishments of the project include the development of several FMUs including distribution and transmission grid models, PV inverters with Volt/Var/Watt controllers, batteries, and predictive optimal controllers. Additionally, a unique software package was developed, called SimulatorToFMU, which is capable of exporting any Python-driven simulator or Python script as an FMU. This is an important contribution towards establishing FMI as one of the main co-simulation standards, because more and more third-party programs for sub-system modeling and simulation are delivered with Python APIs. The CyDER platform was used to perform PV hosting capacity analyses in real utility feeders with and without smart inverter controls, battery storage, and EV charging. Smart inverter controls include conventional Volt/Var/Watt controls for reactive power support and active power curtailment, but also predictive controls that optimize the charging and discharging profile of the battery connected on the DC side in order to minimize the customer’s economic benefit. Finally, an important result of this project is delivering an experimental setup that consists of residential-scale PV inverters with battery storage, a real-time grid simulator with an ideal voltage source as grid emulator, and micro Phasor Measurement Units (PMUs). All these components and additional software modules are coupled to one another using the FMI standard and can be co-simulated with the CyDER platform.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Long-Term field testing of the accuracy and HVAC energy savings potential of occupancy presence sensors in A Single-Family home

The energy-saving potential of occupancy-centric smart thermostats has been extensively explored in simulations but lacked field testing for energy savings quantification and sensor performance assessment in real buildings. This paper presents a long-term field study conducted in a single-family home in Texas, U.S. to evaluate the performance of occupancy-centric controls (OCC) of HVAC (heating, ventilation, and air-conditioning) system in terms of energy savings, sensor accuracy, and impact on electric peak demand. The test site was equipped with a commercial off-the-shelf (COTS) smart thermostat and multiple occupancy presence sensors for OCC implementation. Additionally, a sub-metering system was installed to monitor electricity consumption of various end-use equipment, including the HVAC system. A supplementary device was installed to track the ground-truth occupancy for the accuracy evaluation of the occupancy presence sensor. Scenarios of baseline and OCC controls were alternated weekly over the 20-month testing period. The results indicated an effective OCC execution, as evidenced by indoor temperature profiles. During the 2023 cooling season, OCC achieved total energy savings of 1,958 kWh, corresponding to a 17.6% energy savings ratio. Under certain conditions, daily HVAC energy savings reached as high as 17 kWh, with a savings ratio of 35%. Sensor performance showed an overall accuracy of 83.8%, a False Positive Rate (FPR) of 12.8%, and a False Negative Rate (FNR) of 47.4%. A key limitation was the sensor’s inability to detect stationary occupants during sleep, leading to a midnight FNR of nearly 100% and significantly compromising thermal comfort. Additionally, the implementation of OCC resulted in extended periods of high electricity demand on summer afternoons, affecting occupant’s thermal comfort and posing potential challenges to community-level grid operations if OCC were widely adopted. Furthermore, this study addresses a critical research gap by empirically investigating energy-saving potential and occupancy sensor performance in residential buildings. Through a comprehensive field-testing study, the research examines the interrelationship between sensor accuracy, energy savings, and thermal comfort, an area that has received limited attention in the current literature.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

A Two-Level Model Predictive Control-Based Approach for Building Energy Management including Photovoltaics, Energy Storage, Solar Forecasting and Building Loads

This paper uses a two-level model predictive control-based approach for the coordinated control and energy management of an integrated system that includes photovoltaic (PV) generation, energy storage, and building loads. Novel features of the proposed local controller include (1) the ability to simultaneously manage building loads and energy storage to achieve different operational objectives such as energy efficiency, economic cost efficiency, demand response and grid optimization through the design of specific power trajectory tracking performance functionals, (2) an energy trim function that minimizes the impact of solar forecasting errors on system performance, and (3) the design of a state of charge controller that uses day-ahead forecast of solar power and building loads to intialize energy storage at the start of each day. The local controller is tested in simulation using an exemplary system with PV generation, energy storage and dispatchable building loads. Two sample days with different PV forecasts and multiple case scenarios are considered, and the performance of the algorithm in managing the real and reactive net building load trajectories and the ramp rate of PV injections into the utility network are evaluated. The simulations are based on actual forecasted and measured PV data, and the results show that the local controller meets the tracking requirements for real and reactive power within the operating constraints of the building.

14 SOLAR ENERGY↗

Machine Learning-Driven Optimization of Building Enclosures for Moisture Durability and Thermal Performance

The design of moisture-durable building enclosures with low embodied carbon often involves an iterative process of selecting the materials for the specific exposure conditions to meet the performance requirements. While hygrothermal simulations are commonly used to evaluate moisture durability, they often require advanced expertise for proper implementation. Machine learning (ML) provides a promising alternative by streamlining the design process and minimizing the reliance on complex simulations. This study presents a machine learning-based approach for predicting moisture durability in residential wall assemblies. The ML model was trained to estimate the mold index and maximum moisture content of various layers under typical exposure conditions. The model achieved a high predictive accuracy, with a coefficient of determination (R²) exceeding 0.90 when compared to traditional hygrothermal simulations on materials that were not part of training the ML model. Building on these results, the ML model was developed into a practical tool for optimizing wall assembly designs. This tool allows users to automatically optimize material selections based on energy, moisture, and carbon performance criteria. By incorporating multi-objective optimization, the tool identifies configurations that minimize embodied carbon while maintaining moisture safety and code-compliant thermal performance. Additionally, it provides insights into how material choices influence assembly durability, energy efficiency, and carbon reduction. The tool will be implemented in the Building Science Advisor (BSA) to enhance its performance and provide more granularity on the results. This research highlights the potential for ML-driven tools to simplify the design of high-performance building enclosures, offering architects and engineers a faster, more efficient way to balance critical performance factors.

Salonvaara, Mikael [ORNL] (ORCID:0000000318991554)↗

Evaluation of Phase Change Plaster/Paste Composites for Building Envelopes

Thermal energy storage (TES) materials increase a building’s thermal mass and provide the flexibility to shave and time-shift energy use. These materials are needed to enable grid-interactive efficient buildings, allowing building occupants to lower costs by leveraging free ambient heating/cooling as well as shifting consumption to cheaper electricity periods. Phase change materials (PCMs) that store heat through a solid-liquid/solid-solid transformation are of particular interest for TES because of their ability to store large amounts of energy across a constant temperature or very small temperature range. Furthermore, previous studies on PCM building envelopes evaluated products that may only be incorporated as part of new construction and are not easily applicable to existing, energy-intensive buildings. In this work, we leverage laboratory PCM characterization and whole-building energy simulation to study the energy and cost savings of PCM plaster/paste coatings, which can be applied post construction to both new builds and retrofits of existing buildings (about 40% of residential buildings in the United States fall into the retrofit category). We evaluated the energy impact of PCM plaster/paste composites for a single-family residential building in the United States, including layer design (location and thickness), composite properties (phase change temperature, energy density, and shape of the enthalpy curve), and U.S. climate zone. The enthalpy curve shape of PCM shows that phase change temperature and energy density are not the only two parameters that must be considered for PCM selection. Maximizing the energy density amid the effective TES zone is key to boosting energy savings. We also studied PCM price targets with the aim of achieving reasonable payback periods based on both block and time-of-use electricity rates. Moreover, we developed an empirical equation to provide a rough estimate of the energy savings potential of incorporating PCM without the need to perform detailed simulations. These results will help guide the selection and design of PCM plaster/paste composite elements for residential building envelopes.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗

Physics-informed machine learning for fault-leakage reduced-order modeling

Geologic carbon storage (GCS) is a promising technology for mitigating CO 2 emissions. The overall success of GCS depends on safe operations that are informed by risk assessment and have proper mitigation plans in place. Performing quantitative probabilistic risk assessment for a GCS site using traditional reservoir simulators can be challenging due to the high computational costs. To overcome this challenge, the US Department of Energy’s National Risk Assessment Partnership (NRAP) project has developed an integrated assessment modeling approach that utilizes computationally efficient reduced-order models (ROM) for simulating various parts of a GCS storage site to quantify uncertainty. Here, in this study, we develop a reduced-order model for fault leakage risk assessment. We use a deep learning approach to build the reduced-order model. We perform a sensitivity analysis and find that the deep learning model yields high accuracy with a much smaller computational cost than full-physics simulation. We also evaluate the performance of the model in scenarios where simulations are not possible to run, providing analysis not previously performed in fault-leakage ROM analyses. Based on a sensitivity analysis of the model, we suggest a simplified conceptual model for fault leakage and site monitoring.

58 GEOSCIENCES↗

Retrofitting Buildings with Solar-Reflective Roofs and Walls and its Impact on Peak Power Demand

Buildings are a major consumer of electricity in the United States and a significant portion of the consumption comes from heating, ventilation, and air-conditioning (HVAC) applications. Passive cooling strategies in the building envelope help to reduce the energy consumption for HVAC as well as peak electricity demand. Although being one of the most cost-effective passive cooling strategies, modern reflective roofing and reflective exterior wall technology is not well documented for its impact on peak demand. This study utilized whole building energy simulations on residential and commercial building prototype models to quantify the impact of cool roofs and cool exterior walls. The analysis was performed in three climate zones with varying insulation levels and solar reflectances for roofs and exterior walls. For both the residential and commercial buildings, the baseline building had a roof solar reflectance value of 0.10 and an exterior wall solar reflectance value of 0.25. The results from the simulations show that roofs and exterior walls with higher reflectance values increase cooling energy savings but can also increase heating energy consumption. The impact of changes in solar reflectances was greater in buildings with low roof/wall insulation levels compared to roofs/walls with higher insulation levels. A baseline for the simulations was set with the roof and exterior wall solar reflectances set at 0.1 and 0.25, respectively and simulations having varying roof and exterior wall thermal resistances were compared to the baselines.

14 SOLAR ENERGY↗