BuildingSync: A schema for commercial building energy audit data exchange
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32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
SEARCH · Engineering Papers
Results for “building energy model”
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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Residential Demand Flexibility: Modeling Occupant Behavior using Sociodemographic Predictors
Demand flexibility (DF) has the potential to increase the saturation of renewables in the grid and reduce operating costs for both utilities and customers. However, less than 8% of U.S. residential electric customers are enrolled in DF programs. A major research gap on this topic is an uneven understanding of behavioral drivers of electricity use and DF program participation at the household level. In this study, we employ machine learning models to predict residential occupant behavior in activities relevant to DF. We model occupants' extensive decisions (i.e., choice of action) and intensive behaviors (i.e., amount of time spent) during peak and off-peak time periods using the publicly available American Time Use Survey, which includes activities data for approximately 200,000 respondents. In our machine learning models, predictions for both extensive and intensive behavior fell within a +/-20% error margin at the aggregate level. We identify 13 key sociodemographic predictors of DF-related intensive behavior using LASSO inference and beta coefficient ranking. However, these top predictors differ by activity, suggesting potential scope for differential user targeting for DF events and technologies during program design. This work also contributes to understanding when and who might adopt these DF technologies based on their daily routine activities.
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End-Use Load Profiles for the U.S. Building Stock
This presentation summarizes the items discussed during the End-Use Load Profiles for the U.S. Building Stock: Technical Advisory Group Meeting #4 on September 20, 2019.
30 DIRECT ENERGY CONVERSION↗
An Open-Source Decarbonization Analytics Framework: Designing for Low-Carbon Emission Districts and Communities: Preprint
This paper introduces an open-source analytics framework designed to assist in creating low or net-zero carbon buildings and urban districts. Integrated within URBANopt, an open-source platform for energy analysis in districts and communities, this framework equips researchers, architects, engineers, and other stakeholders with tools to evaluate the carbon footprint implications of their design choices. The framework enables the analysis of various scenarios, incorporating both historical and future emission factors, and can span across different climate zones, each with distinct grid and emissions characteristics. The results showcase the framework's capability to evaluate the impact of design upgrades and control strategies on carbon emissions in districts and communities. An illustrative analysis using a hypothetical district in Denver, Colorado, shows reduced emissions from energy efficiency upgrades and control strategies, highlighting the sensitivity in their effects on emissions and energy use.
buildings energy efficiency↗
Open-cycle thermochemical energy storage for building space heating: Practical system configurations and effective energy density
Salt-hydrate thermochemical materials (TCM) are promising candidates for energy storage systems for building space heating due to their high theoretical energy density and the need for low regeneration temperature. However, water vapor is required to drive the hydration process of the TCM reactor, which poses a challenge during winter when water vapor is typically scarce. Using indoor air directly lowers the building's humidity to an unconformable level in practice, while the cold outdoor air contains limited moisture. Here we consider different integration strategies for open-cycle TCM reactors in buildings and develop a model to simulate their thermal performance across diverse buildings and climates, specifically for building space heating. The potential energy densities and the levelized cost of storage of the TCM reactor are evaluated in practical scenarios to demonstrate the load-shifting potential of TCM systems for heating applications. We use a strontium chloride (SrCl 2 )-based composite as the baseline and explore the impact of various reactor and material changes to the energy density and levelized cost of storage.
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Occupant-driven end use load models for demand response and flexibility service participation of residential grid-interactive buildings
As demand response becomes increasingly used as a tool to support improved grid flexibility, it is important to consider that there are many potential types of energy end uses that may be used to support such flexibility. Residential appliances, often accounting for 30 % or more of residential energy use, are a currently untapped source of demand flexibility, particularly when aggregated together across homes. To date there has been very limited analysis of residential appliances for use as grid-interactive loads. As such, this research uses disaggregated energy end use data for 564 households, to model the electricity demand flexibility potential of the use of residential dishwashers, clothes washers, clothes dryers, ovens, and ranges (oven + stovetop) on both weekdays and weekends. This includes both at the building level, as well as aggregated to the grid level, specifically the Midcontinent Independent System Operator (MISO) region. This study was divided into two parts. Part 1 focuses on determining appliance-level loads, and Part 2, which involves aggregation to the grid. Findings suggest that among the studied appliances, clothes dryers provide the greatest demand reduction potential for most times of the day, followed by dishwashers and clothes washers. The maximum potential reduction for clothes dryers is found to be approximately at 11:00 a.m. and this potential sustains throughout most of the daytime period. When considering the willingness of households to participate, based on a survey of households in the Midwest region, clothes dryers still have the most potential for demand reduction. The availability of appliances for load modulation on weekdays and weekends indicates similar load reduction potential for all appliances. Overall, the results of this study suggest that there is an opportunity for shifting appliance usage to optimize grid efficiency and enhance demand response strategies.
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An assessment of the load modifying potential of model predictive controlled dynamic facades within the California context
Not Available
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Defining the Prototype Convenience Store Building Characteristics
The US Department of Energy supports the development of commercial building energy codes and standards. To support commercial building energy research activities and the development of commercial building energy codes and standards, continuous efforts have been made to convert 30 prototype building models (20 different building types), which cover 80% of US commercial building floor space, to OpenStudio prototype buildings. Additionally, the suite of prototype building models was expanded to include the addition of new building prototype models (e.g., courthouse, college building). This report documents the building and system characteristics of the prototype convenience store building model. Multiple sources, including databases, documented projects, and personal communications, were used to define the prototype convenience store building characteristics, and a one-story, 3,000 ft 2 building was considered as the prototype convenience store to represent an average-sized convenience store in the United States. The Oak Ridge National Laboratory team defined the operational hours of the convenience store as 17 hours per day. The types of exterior walls, roof, and floor were defined as brick wall, metal surfacing roof, and slab-on-grade floor, respectively. Double glazing was selected, and the window-towall ratio was set as 15% on the front side. For the system part, packaged system and centralized water heater were selected for the space heating and cooling system and water heater system, respectively. In terms of the refrigerator system, the number of refrigerators and freezers were determined as eight closed cases and two walk-in units.
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Selection of Global Climate Model Data for Downscaling With Generative Machine Learning and Use in the Power Planning for Alignment of Climate and Energy Systems Project
The range of results from climate models and scenarios is important to the understanding of uncertainty in power planning analysis. A U.S. Department of Energy-funded analytic project called Power Planning for Alignment of Climate and Energy Systems is developing data and analytic methods to reflect the effects of climate change on key variables for power system planning, as part of the Grid Modernization Lab Consortium. This project will select and prepare global climate model results for use in power system planning models. A related report (Evaluation of Global Climate Models for Use in Energy Analysis) assesses the performance of various global climate models from the Coupled Model Intercomparison Project Phase 6 data archive for their historical skill with respect to energy system performance and for their future projections under multiple climate change scenarios. Building from that report, we describe the selection of a climate scenario (Shared Socioeconomic Pathway [SSP] 2-4.5) and five climate models: TaiESM1, EC-Earth3-CC, GFDL-CM4, EC-Earth3-Veg, and MPI-ESM1-2-HR. We describe the model selection criteria, which were based on the quality of the match between model results under historical conditions and on the representation of the range of future values for several variables. These results will be downscaled via an open-source generative machine learning method called Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts.
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Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement
The digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch of data-driven smart city applications for efficient and sustainable urban management. Despite their effectiveness, these applications often rely on massive amounts of high-dimensional and multi-domain data for monitoring and characterizing different urban sub-systems, presenting challenges in application areas that are limited by data quality and availability, as well as costly efforts for generating urban scenarios and design alternatives. As an emerging research area in deep learning, Generative Artificial Intelligence (GenAI) models have demonstrated their unique values in content generation. This paper aims to explore the innovative integration of GenAI techniques and urban digital twins to address challenges in the planning and management of built environments with focuses on various urban sub-systems, such as transportation, energy, water, and building and infrastructure. The survey starts with the introduction of cutting-edge generative AI models, such as the Generative Adversarial Networks (GAN), Variational Autoencoders (VAEs), Generative Pre-trained Transformer (GPT), followed by a scoping review of the existing urban science applications that leverage the intelligent and autonomous capability of these techniques to facilitate the research, operations, and management of critical urban subsystems, as well as the holistic planning and design of the built environment. Based on the review, we discuss potential opportunities and technical strategies that integrate GenAI models into the next-generation urban digital twins for more intelligent, scalable, and automated smart city development and management.
3D city modeling↗
Utilizing AI and Spatial Data to Identify & Rapidly Disseminate Energy Infrastructure Insights
GeoGov Summit Final Presentation entitled "Utilizing AI and Spatial Data to Identify & Rapidly Disseminate Energy Infrastructure Insights". Maintaining the integrity of energy infrastructure plays a critical role in ensuring energy security. Robust foundational AI models using data from federal, state, industry, and other sources can help address integrity risk management & mitigation issues as well as evaluate extended use strategies. Trusted foundational models can help with industry adoption and accelerate innovation by enhancing integrity predictions, reduce costs, and informing infrastructure build-out. Coordination, collaboration & data sharing to develop robust models to aid in: Optimizing operations; Minimizing costs; Ensuring energy security.
Advanced Infrastructure Integrity Model (AIIM)↗
Dynamic metrics of natural ventilation cooling effectiveness for interactive modeling
The evaluation of natural ventilation potential for cooling indoor spaces during the early design phases is of great interest to researchers and practitioners. Among various definitions and usages for natural ventilation potential (NVP) in early design evaluation, this paper reviews and identifies the key performance indicators, and proposes two new dynamic metrics—natural ventilation cooling effectiveness (NVCE) and climate potential utilization ratio (CPUR). The metrics are dynamically responsive to various design options, in both steady and transient states, allowing consideration of thermal mass. Assisting in design development processes, the metrics quantify how well indoor spaces make use of natural ventilation’s cooling capacity. Case studies are presented to demonstrate how NVCE and CPUR enable designers to evaluate the predicted performance and how to apply the information to improve building design. Finally, the results of the design iterations showed that the relationship among various design parameters should be dynamically understood in order to evaluate the performance of natural ventilation, confirming that “the more the airflow, the greater the potential,” and “the heavier the thermal mass, the greater the energy saving” were not always true.
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Aggregation and data driven identification of building thermal dynamic model and unmeasured disturbance
An aggregate model is a single-zone equivalent of a multi-zone building, and is useful for many purposes, including model based control of large heating, ventilation and air conditioning (HVAC) equipment. This paper deals with the problem of simultaneously identifying an aggregate thermal dynamic model and unknown disturbances from input–output data of multi-zone buildings. The unknown disturbance is a key challenge since it is not measurable but non-negligible. In this paper, we first present a principled method to aggregate a multi-zone building model into a single zone model, and show the aggregation is not as trivial as it has been assumed in the prior art. We then provide a method to identify the parameters of the model and the unknown disturbance for this aggregate (single-zone) model. Finally, we test our proposed identification algorithm to data collected from a multi-zone building testbed in Oak Ridge National Laboratory. A key insight provided by the aggregation method allows us to recognize under what conditions the estimation of the disturbance signal will be necessarily poor and uncertain, even in the case of a specially designed test in which the disturbances affecting each zone are known (as the case of our experimental testbed). This insight is used to provide a heuristic that can be used to assess when the identification results are likely to have high or low accuracy.
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PANDEMIC: Occupancy driven predictive ventilation control to minimize energy consumption and infection risk
During the SARS-CoV-2 (COVID-19) pandemic, governments around the world have formulated policies requiring ventilation systems to operate at a higher outdoor fresh air flow rate for a sufficient time, which has led to a sharp increase in building energy consumption. Therefore, it is necessary to identify an energy-efficient ventilation strategy to reduce the risk of infection. In this study, we developed an occupant-number-based model predictive control (OBMPC) algorithm for building ventilation systems. First, we collected the occupancy and Heating, ventilation, and air conditioning system (HVAC) data from March to July 2021. Then, four different models (Auto regression moving average-based multilayer perceptron (ARMA_MLP), Recurrent neural networks (RNN), Long short-term memory networks (LSTM), and Nonhomogeneous Markov with change points detection (NH_Markov)) were used to predict the number of room occupants from 15 min to 24 h ahead with an interval output. We found that each model could predict the number of occupants with 85% accuracy using a one-person offset. Furthermore, the accuracy of 15 min of the ahead prediction could reach 95% with a one-person offset, but none of them could track abrupt changes. The occupancy prediction results were used to calculate the ventilation demand using the Wells-Riley equation, and the upper bound can maintain an infection risk lower than 2% for 93% of the day. This OBMPC model could reduce the coil load by 52.44% and shift the peak load by 3 h up to 5 kW compared with 24 × 7 h full outdoor air (OA) system when people wear masks in the space. The occupancy prediction uncertainty could cause a 9% to 26% difference in demand ventilation, a 0.3°C to 2.4°C difference in zone temperature, a 28.5% to 44.5% difference in outdoor airflow rate, and a 10.7% to 28.2% difference in coil load.
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Thermal performance and energy consumption validation of an occupied local government office building outfitted with ceiling tile phase change materials
Buildings present an opportunity for energy conservation and the modulation of peak energy demand through controlled Heating, Ventilation, and Air Conditioning (HVAC) energy use. The administrative and office building stock in the United States holds potential to achieve energy and demand savings through retrofits such as insulation, weatherization, and thermal energy storage. Specifically, there is a need to validate passive phase change material (PCM) applications in full scale in aging administrative buildings in the US to evaluate the energy benefits. Aim of this study was to conduct a whole building level thermal and energy validation of an operational building and explore an alternative method for evaluating energy efficiency. To accomplish this, the study employed PCMs in the drop ceiling and carry out an energy audit and on-site measurement of HVAC systems' energy demand and consumption. A full-scale EnergyPlus energy model, modeled by the authors, served as a baseline for evaluation. The results show that calibrated model's envelope temperature measures fall within the accepted errors. HVAC energy simulation results also fall within the accepted errors for monthly and hourly pre- and post- PCM retrofit electricity and natural gas data. The novelty of this study is that it employees energy scales per Heating Degree Hour and Cooling Degree Hour, in contrast to the commonly used Heating Degree Days and Cooling Degree Days as reported in the literature to analyze energy savings. These findings underscore the pivotal role of a calibrated model in assessing the efficacy of a singular energy measure, like a PCM-retrofitted ceiling, in an occupied office building.
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Decay heat analysis for advanced reactor spent fuel transportation and storage applications
Accurate characterization of nuclide inventories and decay heat in spent nuclear fuel is critical for ensuring its safe handling, storage, transportation, and disposal. Although extensive research has been conducted on light-water reactor fuel, advanced reactors present unique challenges due to their diverse core configurations, fuel characteristics, neutron energy spectra, and burnup levels. Building upon previous efforts that developed representative reactor core models for various advanced reactor types and fuels, this study evaluates reactor-specific decay heat characteristics. The results highlight significant variations across advanced reactor types as well as across reactor designs within the same reactor type, and they provide comparison to typical commercial light-water reactor fuel. For example, thermal-spectrum reactor fuels were observed to have an approximately 100-fold decrease in decay heat over the first decade of cooling, whereas the reduction was 10-fold for fast-spectrum reactor fuels. Mass-specific decay heat at discharge can differ by three orders of magnitude among the fast and thermal reactor systems considered. Overall, for the analyzed advanced reactor fuel, fewer than 17 nuclides account for over 99% of total decay heat at 0.5 years, and that number drops to fewer than 7 nuclides at 100 years of cooling. By quantifying reactor-specific decay heat trends and nuclide contributions, this work provides a technical basis to support the development of spent fuel management strategies for advanced reactor fuels as well as safety evaluations for storage, transportation, and long-term waste disposal.
Advanced reactors↗
Integrating adaptive learning with post hoc model explanation and symbolic regression to build interpretable surrogate models
Abstract We develop a materials informatics workflow to build an interpretable surrogate model for micromagnetic simulations. Our goal is to predict the energy barrier of a moving isolated skyrmion in rare-earth-free $$\hbox {Mn}_4$$ Mn 4 N. Our approach integrates adaptive learning with post hoc model explanation and symbolic regression methods. We discuss an unexplored acquisition function (information condensing active learning) within the adaptive learning loop and compare it with the known standard deviation function for efficient navigation of the search space. Model-agnostic post hoc explanation techniques then uncover trends learned by the trained model, which we then leverage to constrain the expressions used for symbolic regression. Graphical abstract
Biswas, Ankita↗
Deep Reinforcement Learning for Autonomous Water Heater Control
Electric water heaters represent 14% of the electricity consumption in residential buildings. An average household in the United States (U.S.) spends about USD 400–600 (0.45 ¢/L–0.68 ¢/L) on water heating every year. In this context, water heaters are often considered as a valuable asset for Demand Response (DR) and building energy management system (BEMS) applications. To this end, this study proposes a model-free deep reinforcement learning (RL) approach that aims to minimize the electricity cost of a water heater under a time-of-use (TOU) electricity pricing policy by only using standard DR commands. In this approach, a set of RL agents, with different look ahead periods, were trained using the deep Q-networks (DQN) algorithm and their performance was tested on an unseen pair of price and hot water usage profiles. The testing results showed that the RL agents can help save electricity cost in the range of 19% to 35% compared to the baseline operation without causing any discomfort to end users. Additionally, the RL agents outperformed rule-based and model predictive control (MPC)-based controllers and achieved comparable performance to optimization-based control.
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