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Presentation for INL's AI/ML Symposium. Topic: Evaluation of AI-generated documents for nuclear permitting/licensing.
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The National Reactor Innovation Center (NRIC) is leading a transformative initiative to accelerate advanced reactor deployment by fundamentally reimagining how nuclear safety basis documentation is developed, reviewed, and maintained. Traditional Documented Safety Analysis (DSA) processes for DOE-authorized facilities rely on static, document-centric workflows that consume significant time and resources, exemplified by recent major licensing efforts requiring hundreds of thousands of staff hours and millions of pages of documentation review. These conventional approaches create barriers to the rapid, cost-effective deployment of advanced reactors that America's future energy needs demand. NRIC's DOE Authorization Digital Transformation Project addresses these challenges through an innovative framework that integrates artificial intelligence (AI), digital engineering, and systems-based data management into a cohesive digital ecosystem. This white paper presents NRIC's methodology for evaluating AI-enabled document generation capabilities within this broader digital infrastructure, using the Demonstration of Microreactor Experiments (DOME) facility as a pilot case study. The evaluation will assess an AI tool's ability to generate a Preliminary Documented Safety Analysis (PDSA) through progressive integration stages—from standalone document processing to full digital thread connectivity—while maintaining rigorous verification, validation, and regulatory acceptance standards. By establishing dynamic, traceable connections between design data and safety documentation, NRIC's approach has the potential to reduce both document development time and regulatory review cycles by as much as 50%, while simultaneously improving accuracy, consistency, and traceability. This initiative represents a critical step toward establishing reusable digital infrastructure that reactor developers can leverage to accelerate their path from concept to commercial operation, directly supporting NRIC's mission to demonstrate and deploy advanced nuclear energy technologies.
The National Environmental Policy Act of 1969, as amended (NEPA), is a major environmental law in the United States, requiring Federal agencies to consider and document potential environmental impacts before deciding on a proposed action. Modernization of NEPA and permitting processes faces significant challenges due to the lack of standardized formats and interoperable systems for organizing and sharing NEPA-related information across agencies. Much of the information gathered during NEPA reviews is written into documents such as categorical exclusions, environmental assessments, and environmental impact statements, then filed in predominately independent agency file stores that may or may not be publicly accessible. The application of metadata and data standards, such as those recommended by the Council on Environmental Quality (CEQ), to NEPA documents offers a shared vocabulary and structure for key entities like projects, processes, and documents that can streamline information exchange and enhance collaboration across systems. In this work, we publicly release NEPATEC2.0, an expanded corpus of NEPA documents with associated metadata. NEPATEC2.0 encompasses approximately 120,000 documents from 60,000 projects prepared by more than 60 different agencies. Modeled to align with CEQ metadata standards, NEPATEC2.0 promotes consistency in environmental reviews and supports the ongoing effort to modernize permitting technologies by facilitating more transparent, efficient, and data-driven decision-making. Importantly, NEPATEC2.0 demonstrates the possibilities and limitations of large language model-based prompting to extract information from NEPA documents at scale.
Safety basis documentation development and review under U.S. Department of Energy (DOE) authorization have emerged as critical constraint throttling deployment of advanced nuclear reactors, with traditional processes demanding extraordinary resource investment that delays the delivery of these technologies. Traditional Documented Safety Analysis (DSA) processes rely on static documents with limited traceability [U.S. DOE]. The regulatory review and engagement processes are similarly constrained, often requiring significant effort and extensive manual verification. The scale of this challenge is exemplified by the U.S. Nuclear Regulatory Commission (NRC) review of the NuScale application, which required over 250,000 staff hours and the evaluation of approximately two million pages of documentation [Bergman 2021]. The volume and complexity of information within nuclear licensing applications or authorization reviews demands innovative approaches to document generation and data management.
The purpose of this document is to create a single reference document representing abridged versions of the PATMOS-x Algorithm Theoretical Basis Documents (ATBDs) specifically relevant to GOES 16-18 processing. This document has been compiled from several documents: Cloud Mask ATBD, AWG Cloud Height Algorithm (ACHA) ATBD, Daytime Cloud Optical and Microphysical Properties (DCOMP) ATBD, and CLAVR-x User's Guide.
The goal of this work is to develop energy efficiency, demand flexibility, and other retrofit end-use load shapes (electricity, gas, propane, or fuel oil) that cover a majority of the high-impact, market-ready (or nearly market-ready) measures. "Measures" refers to retrofits that can be applied to buildings during modeling. An "end-use savings shape" is the difference in energy consumption between a baseline building and a building with an energy efficiency, demand flexibility, or other retrofit measure applied. It results in a time-series profile that is broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step. ResStock is a highly granular, physics-based, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual subhourly energy consumption of the residential building stock across the United States. The baseline model intends to represent the U.S. residential building stock as it existed in 2018. Technical documentation for the inputs and assumptions in the baseline building stock model is available in Reyna et al. (2025). Calibration and validation of the baseline model results are available in the final technical report of the End-Use Load Profiles project (Wilson, et al. 2022). This document focuses on a single end-use savings shape measure: Residential Two-Stage Geothermal Heat Pump (GHP) (4.0 COP, 20.5 EER) With Envelope Improvements. This measure combines a two-stage GHP with envelope improvements as a single package. As this package is a combination of two other measures, this document focused on documenting the results associated with this combination of technologies, with individual measure documents for two-stage GHPs and envelope improvements providing the information on the details of these measures. When the two technologies are combined, envelope improvements can modestly reduce energy consumption by a further 10%-15%, but also reduce the required size of the ground heat exchanger and heat pump by approximately 33% on average across all sites. The cost of installing envelope improvements in these homes is likely to be more than paid for by the reduction in equipment and drilling costs in these buildings for the majority of the stock.
The goal of this work is to develop energy efficiency, demand flexibility, and other retrofit end-use load shapes (electricity, gas, propane, or fuel oil) that cover a majority of the high-impact, market-ready (or nearly market-ready) measures. "Measures" refers to retrofits that can be applied to buildings during modeling. An "end-use savings shape" is the difference in energy consumption between a baseline building and a building with an energy efficiency, demand flexibility, or other retrofit measure applied. It results in a time-series profile that is broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step. ResStock (TM) is a highly granular, physics-based, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual subhourly energy consumption of the residential building stock across the United States. The baseline model intends to represent the U.S. residential building stock as it existed in 2018. Technical documentation for the inputs and assumptions in the baseline building stock model is available in Reyna et al. (2025). Calibration and validation of the baseline model results are available in the final technical report of the End-Use Load Profiles project (Wilson et al. 2022). This documentation focuses on a single end-use savings shape measure: Residential Variable-Speed Geothermal Heat Pump (GHP). This document provides the relevant new modeling information for variable-speed systems not previously covered in either the single-stage or two-stage documents. Variable-speed GHPs represent the most efficient option available for this technology: They provide the most savings, with up to 46% for the applicable portion of the housing stock, compared to 31% for less efficient single-stage GHPs. Additional results shown here detail how the savings change for sections of the housing stock with different base heating fuels and in different climate zones, and they show the savings potential by state for both heating and cooling. Utility bills and electric panel impacts are also shown and discussed.
The goal of this work is to develop energy efficiency, demand flexibility, and other retrofit end-use load shapes (electricity, gas, propane, or fuel oil) that cover a majority of the high-impact, market-ready (or nearly market-ready) measures. "Measures" refers to retrofits that can be applied to buildings during modeling. An "end-use savings shape" is the difference in energy consumption between a baseline building and a building with an energy efficiency, demand flexibility, or other retrofit measure applied. It results in a time-series profile that is broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step. ResStock is a highly granular, physics-based, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual subhourly energy consumption of the residential building stock across the United States. The baseline model intends to represent the U.S. residential building stock as it existed in 2018. Technical documentation for the inputs and assumptions in the baseline building stock model is available in Reyna et al. (2025). Calibration and validation of the baseline model results are available in the final technical report of the End-Use Load Profiles project (Wilson et al. 2022). This document focuses on a single end-use savings shape measure: Residential Two-Stage Geothermal Heat Pump (4.0 COP, 20.5 EER). This document builds on details established in the single-stage document (Maguire et al. 2025) to detail differences in the approach to modeling this higher efficiency, but more commonly deployed, type of geothermal heat pump. Specific EnergyPlus objects and product specific curves used are highlighted along with showing the results of this measure compared to the baseline and single-speed geothermal heat pumps. Two-speed geothermal heat pumps are able to save even more energy and on utility bills than single-speed products, albeit at the expense of a higher first cost.
To reduce the cost and time needed for regulatory compliance, nuclear power plants (NPPs) can utilize artificial intelligence (AI) to assist in interpreting complex and voluminous documents that typically span thousands of pages. Usually, the process of interpreting a plant’s technical specifications (TSs) and associated documents is labor intensive. This study aims to understand what processes state-of-the-art large language models (LLMs) can automate and to identify the pitfalls associated with using LLMs to reduce human labor costs and time. This research uses a recent AI technology called retrieval augmented generation (RAG), which retrieves pages of information from TSs and associated documents to assist with NPP power uprates (cleared to produce more power). LLMs are integral to RAG because they create human-like responses based on the retrieved information, aiding in the interpretation and application processes. A baseline case demonstrates how LLMs can operate successfully for a power uprate application. Then five use cases show five types of potential failures: (1) RAG retrieving the incorrect information, (2) RAG misinterpreting the retrieved information, (3) RAG relying on knowledge not contained in the retrieved information, (4) RAG hallucinating, and (5) RAG refusing to answer. The results of the five use cases suggest that automating the human interpretation of TSs and associated documents with AI should be approached with caution. A subject-matter expert reviewed the AI outputs from the five use cases and concluded that an LLM can produce technical information that is needed to produce power uprate applications in certain instances.
Artificial intelligence has transformed building science research over the past decade, with applications spanning energy modeling, energy prediction, HVAC optimization and controls, fault detection, and occupancy modeling. However, many studies lack adequate documentation of datasets, algorithms, training procedures, and validation methods. Building science research faces additional challenges including inconsistent evaluation metrics, limited generalizability across building types, climates, and significant gaps between experimental studies and deployed systems. This communication provides practical guidance for good practices in documenting and publishing AI-based research following established standards from the computer science and machine learning communities. By adopting frameworks such as Datasheets for Datasets, Model Cards, and standardized reproducibility checklists, researchers can ensure their work meets the rigorous documentation standards necessary for reproducible, comparable, and impactful building science research.
Despite the dramatic progress in Large Language Model (LLM) development, LLMs often provide seemingly plausible but not factual information, often referred as hallucinations. Retrieval-augmented LLMs provide a non-parametric approach to solve these issues by retrieving relevant information from external data sources and augment the training process. These models helps to trace evidence from an externally provided knowledge base allowing the model predictions to be better interpreted and verified. In this work, we critically evaluate these models in their ability to perform in scientific document reasoning tasks. To this end, we tuned multiple such model variants with science-focused instructions and evaluated them on a scientific document reasoning benchmark for the usefulness of the retrieved document passages. Our findings suggest that models justify predictions in science tasks with fabricated evidence and leveraging scientific corpus as pretraining data does not alleviate the risk of evidence fabrication.
U.S. Average Benchmark life cycle inventory documentation for market carbon dioxide production for calendar years 2018 - 2022. The documentation includes a technical report describing data sources and statistical methods used and an Excel file showing calculations. The calculations performed are used in the 45Q benchmark json-ld dataset, compatible with the NETL CO2U LCA Guidance Toolkit. Summary impact results will be included in the NETL CO2U LCA Documentation Spreadsheet within the Toolkit. To access the model referenced in the report, please visit https://www.netl.doe.gov/energy-analysis/details?id=21915ba8-d2cf-43ea-b9b2-1d76363a46a7
This documentation focuses on a single end-use savings shape measure—heat pump rooftop units with supplemental heat that matches the original fuel type of the replaced system; if the existing system used electric resistance heating, the supplemental heating source is electric resistance. If it was a natural gas furnace, the supplemental system is modeled as natural gas. This is a modification to the heat pump rooftop unit with electric supplemental heat measure from the Commercial EUSS 2023 Release 1 dataset. This document will primarily discuss the supplemental heating change for the heat pump RTU measure. For a comprehensive overview of the fundamental modeling methodology and background of the heat pump RTU measure, including performance curves and other key assumptions, please review the documentation for the original heat pump rooftop unit with electric supplemental heat.
This documentation focuses on a single end-use savings shape measure - heat pump rooftop units (HP-RTUs) with standard efficiencies that are prevalent in the current market. Today, these products are claimed as standard efficiency by the manufacturers and are most commonly installed. This document will primarily discuss the additional changes of performance and configuration of the standard efficiency HP-RTUs while a comprehensive overview of the fundamental modeling methodology and background of the HP-RTU measure, including applicability, sizing scheme, and other key assumptions can be found in the original documentation: Heat Pump RTU. The definition of "standard efficiency" can be a bit vague when considering detailed specifics other than efficiency metrics of a heat pump. Thus, we have focused on reflecting products where manufacturers claim as standard efficiency and products that are not top-of-the-line in their model lineup. Based on gathering information from eight products from four major manufacturers, there are some common (or relatively prevalent) characteristics and we have defined standard efficiency product as follows: direct drive outdoor unit fan, two stages of heat pump cooling, single stage heat pump heating (i.e., all compressors running at the same time), heat pump minimum lockout temperature of 0°F (-17.8°C), backup electric resistance heating, backup heating runs at the same time as heat pump heating , heat pump heating locking out below minimum operating temperature, IEER in between 11-13, and HSPF in between 8-8.9. There are cases where some of these products have other variations: a variable frequency drive fan, dual fuel (i.e., gas backup heating) option, three or more cooling stages, two stages of heat pump heating, no backup heating as default , etc. However, this work tried to reflect the most common and prevalent options for a standard efficiency product.
Design Basis Documents / Owner’s Technical Specifications are developed for parabolic trough and central receiver power plants using nitrate salt as the heat transport fluid and the thermal storage medium. The goals were to 1) distill the successful experience with nitrate salt systems from as many commercial projects as possible, 2) provide technical bases for equipment design/selection that an owner can impose on an EPC contractor, 3) compile this information in one location to provide guidance on salt systems that is as broadly applicable to as many projects as possible, and 4) work toward an industry consensus on a Design Basis Document that reflects the lessons learned from earlier commercial projects. The document allows an owner to provide design guidance, and to impose a minimum set of requirements, above and beyond those in the normal Codes and Standards, on an EPC contractor in an effort to avoid a repeat of past mistakes. If successful, this would allow salt systems to achieve the same levels of reliability and availability as commercial parabolic trough plants using diphenyl oxide / biphenyl (i.e., Dowtherm A, Therminol VP-1) as the heat transfer fluid. The report consists of 3 volumes: • Volume 1 - Specifications for Parabolic Trough Projects • Volume 2 - Specifications for Central Receiver Projects • Volume 3 - Narrative Volumes 1 and 2 include discussions of the following topics: • Plant functional requirements for the salt systems • Plant operating states, and transitions between states, for the salt systems • Risk analysis of the principal salt components • Plant requirements for the salt systems to meet the functional and the operating requirements • Type of specification for the principal salt components: functional; or prescriptive • Current state of the art for salt systems. Volume 3 discusses a number of cases in which the salt equipment at commercial parabolic trough and central receiver projects has not met the projected levels of reliability and availability. Possible reasons for the sources of the problems are discussed, as is a range of possible alternate designs that could avoid the known problems.
GridLAB-D is a power distribution systems simulation and analysis tool developed by Pacific Northwest National Laboratory. Although GridLAB-D and its underlying residential load model has been used in a variety of power systems analyses, comprehensive documentation of the derivation of the residential house model has not been formally published. The purpose of this technical support document is to serve as a comprehensive resource for the thermal dynamics modeling and house definition implemented in GridLAB-D. It covers model derivation, parameter definitions, and implementation steps in the codebase.
Building on the successfully completed effort to calibrate and validate the U.S. Department of Energy's ResStock and ComStock models over the past 3 years, the objective of this work is to produce national data sets that empower analysts working for federal, state, utility, city, and manufacturer stakeholders to answer a broad range of analysis questions. The goal of this work is to develop energy efficiency, electrification, and demand flexibility end-use load shapes (electricity, gas, propane, or fuel oil) that cover a majority of the high-impact, market-ready (or nearly market-ready) upgrade measures, or upgrades. "Measures" refers to energy efficiency variables that can be applied to buildings during modeling. An end-use savings shape is the difference in energy consumption between a baseline building and a building with an energy efficiency, electrification, or demand flexibility upgrade applied. It results in a time-series profile that is broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step. ComStock is a highly granular, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual subhourly energy consumption of the commercial building stock across the United States. The baseline model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology and results of the baseline model are discussed in the final technical report of the End-Use Load Profiles project. This documentation focuses on an upgrade package of three end-use savings shapes upgrades - Window Replacement, Exterior Wall Insulation, and Roof Insulation, which we will refer to collectively as the "High Efficiency Envelope" package. More details on the individual upgrades can be found on the ComStock Measures Documentation page. An upgrade package applies two or more EUSS upgrades to a single building model simulation. Since ComStock is a bottom-up physics-based model, an upgrade package will go beyond aggregating or summing the individual upgrade results and produce novel results by simulating interactions between the upgrades. For example, pairing an envelope upgrade with an electrification upgrade would likely result in higher savings results than the sum of these upgrades individually, and the size of the heating, ventilating, and air conditioning (HVAC) equipment may be reduced if the envelope upgrade reduces the loads significantly.