The operation of service hot water systems during peak demand hours can significantly increase costs for customers and cause burden on the grid. The integration of service hot water thermal storage and load management strategies holds promise for alleviating peak demands, reducing costs for customers, and enhancing grid resilience. Existing studies have investigated load management strategies for service hot water thermal storage. However, typical load management strategies often require either a specific thermal storage system design or an advanced control method, which can result in extra cost or effort. Additionally, the existing studies typically focus on a single building type or climate zone. It is crucial to note that the performance of service hot water thermal storage load management can vary significantly across different building types and climate zones due to diverse system configurations, water draw profiles, and occupant requirements. Recognizing the research gaps, this study aims to provide service hot water load management strategies without adding significant extra cost or effort. We conduct a comprehensive analysis by considering multiple commercial building types and climate zones across the United States. We analyze detailed hourly results from a micro perspective and the nation-wide savings by aggregating results from various building types and climate zones from a macro perspective. Cost intensity saving percentage is used as the metric to evaluate performance. The findings provide valuable knowledge for policymakers or energy planners. Additionally, it highlights avenues for future research and development to further enhance the integration of service hot water thermal storage and load management strategies.

Building officials, particularly those in resource-constrained or rural jurisdictions, face labor-intensive, error-prone, and costly manual reviews of design documents as projects increase in size and complexity. The growing adoption of Building Information Modeling (BIM) and Large Language Models (LLMs) presents opportunities for automated code review (ACR) solutions. This study introduces a novel agent-driven framework that integrates BIM-based data extraction with automated verification using both retrieval-augmented generation (RAG) and Model Context Protocol (MCP) agent pipelines. The framework employs LLM-enabled agents to extract geometry, schedules, and system attributes from heterogeneous file types, which are then processed for building code checking through two complementary mechanisms: (1) direct API calls to the US Department of Energy COMcheck engine, providing deterministic and audit-ready outputs, and (2) RAG-based reasoning over rule provisions, enabling flexible interpretation where coverage is incomplete or ambiguous. The framework was evaluated through case demonstrations, including automated extraction of geometric attributes (such as surface area, tilt, and insulation values), parsing of operational schedules, and validation of lighting allowances under ASHRAE Standard 90.1-2022. Comparative performance tests across multiple LLMs showed that GPT-4o achieved the best balance of efficiency and stability, while smaller models exhibited inconsistencies or failures. Results confirm that MCP agent pipelines outperform RAG reasoning pipelines in rigor and reliability. This work advances ACR research by demonstrating a scalable, interoperable, and production-ready approach that bridges BIM with authoritative code review tools.

The rapid evolution of Artificial Intelligence (AI) across various sectors offers unprecedented opportunities for the building industry, which faces critical challenges in sustainability and efficiency. This review paper traces the development of AI in building studies, from initial applications of basic AI to advanced implementations involving machine learning (ML), deep learning (DL), and generative AI (Gen AI). While considerable advancements have been made, significant gaps remain in integrating these technologies, particularly Gen AI, seamlessly into building research. The paper reviews existing studies that utilize Gen AI in the building field and discusses both opportunities, such as leveraging AI for autonomous, real-time adaptive experimentation and operational roles, and challenges, including risks of misinformation, accountability issues, and security vulnerabilities. It also outlines the potential future directions for applying Gen AI more effectively in the building sector and emphasizes the need for further research to overcome current limitations and fully harness AI's potential for sustainable development.

Ensuring grid stability becomes increasingly critical for effective heating in cold climates. However, natural disasters, especially during winter, pose significant threats to grid stability, impacting the reliability of air-source heat pumps. Current studies predominantly focus on resilience at the grid level, with limited attention given to source-side resilience, such as the integration of renewable energy sources and storage solutions into heating systems. This paper delves into the literature on renewable-powered heat pumps to assess their potential in enhancing building resilience in U.S. cold climate zones. By leveraging renewable sources—solar, geothermal, and water—in conjunction with heat pump technology and supported by thermal or battery storage, this approach aims to provide a dependable solution for maintaining indoor heating during grid failures. Our analysis begins with a review of various renewable energy sources suitable for heat pumps, followed by an exploration of their application in cold climate regions across the U.S., and discussions on potential integration strategies with heat pump systems. This study highlights the advantages and suitability of solar irradiance and geothermal resources, emphasizing the importance of tailored, site-specific assessments to maximize energy efficiency and resilience. Additionally, it outlines the economic and environmental considerations necessary for implementing such systems and identifies potential challenges and areas for future research to facilitate the broader integration of renewable energy in heating solutions for enhanced resilience.

This paper investigates the transformative potential of Generative AI (Gen-AI) technologies, particularly large language models, within the building industry. By leveraging these advanced AI tools, the study explores their application across key areas such as automated compliance checking and building design assistance. The research highlights how Gen-AI can automate labor-intensive processes, significantly improving efficiency and reducing costs in building practices. The paper first discusses the two widely applied fundamental models-Transformer and Diffusion model-and summarizes current pathways for accessing Gen-AI models and the most common techniques for customizing them. It then explores applications for text generation, such as compliance checking, control support, data mining, and building simulation input file editing. Additionally, it examines image generation, including direct generation through diffusion models and indirect generation through language model-supported template creation based on existing Computer-Aided Design or other design tools with rendering. The paper concludes with a comprehensive analysis of the current capabilities of Gen-AI in the building industry, outlining future directions for research and development, with the goal of paving the way for smarter, more effective, and responsive design, construction, and operational practices.

This paper presents a comprehensive analysis of the role of Direct Current (DC) power systems in enhancing residential energy efficiency, integrating renewable energy sources, and promoting sustainability. The study systematically explores the application of DC power from generation to end-use in households, emphasizing its compatibility with modern appliances and addressing efficiency challenges, particularly in motor-based DC applications. A detailed market survey of thermoelectric, DC, and Alternating Current refrigerators provides insights into their availability and cost-effectiveness. The integration of DC systems with renewable sources like solar photovoltaics and micro hydropower is examined, considering the influence of regional resource availability and regulatory frameworks. The paper also highlights recent advancements in converter technology critical for DC implementation, focusing on system efficiency and innovative energy storage and management solutions. Additionally, a numerical analysis based on ResStock offers a quantitative assessment of potential energy savings. Despite the significant benefits of DC systems, such as improved energy efficiency and reduced environmental impact, the study acknowledges challenges, including the need for standardization, market gaps for DC-compatible appliances, integration complexities, and the necessity for supportive regulatory policies. This research contributes to the understanding of residential DC power systems and proposes strategies for a balanced adoption that optimizes benefits while addressing inherent challenges.

Building-level loads and load schedules prescribed by current modeling rules save modelers time and provide standards during whole building performance modeling. However, recent studies show that they sometimes insufficiently capture the entire building performance due to the varied loads and load schedules for different space types. As a solution to this issue, this paper presents a database of default building-space-specific loads and load schedules for use in energy modeling, and in particular code compliance modeling for commercial buildings. The existing sets of default loads and load schedules are reviewed and the challenges behind using them for specific research topics are discussed. Then, the proposed method to develop the building-space-specific loads and load schedules is introduced. After that, the database for these building-space-specific loads and load schedules is presented. In addition, one case is studied to demonstrate the applications of these loads and load schedules. In this case study, three methods are used to develop building energy models: space-specific (using knowledge of the distribution and location of space types and applying the space-specific data in the developed database), building-level (assuming a lack of knowledge of the space types and using the building-level data in the developed database), and calculated-ratio (assuming knowledge of the distribution of space types but not their locations and calculating weighted average values based on the space-specific data in the developed database). The energy results simulated by using these three methods are compared, which shows building-level methods can produce significantly different absolute energy and energy savings results than the results using space-specific methods. Finally, this paper discusses the application scope and maintenance of this new database.

For decades, building energy simulation has been used by architects, engineers, and researchers to evaluate the performance of building designs.Heating, ventilation, and air-conditioning systems are one of the main energy end-users in buildings; hence, it is important to reasonably capture the performance of this equipment in simulations at rated (as defined, for instance, in AHRI and ASHRAE Standards (AHRI, 2020a, 2020b; ASHRAE, 2019)), full load, and part load conditions.Copper is a performance curve generator created to enable building energy simulation practitioners to generate simulation-ready performance curves for heating and cooling equipment that not only capture the equipment's typical behavior at part load, but also match a set of design characteristics, including full load and part load efficiencies.

Despite the abundance of research applications of system modeling in grid-interactive efficient buildings (GEBs), the transfer of those applications to real-world practices is still in its early stages. This is partially due to the lack of a summary on how system modeling should be established for a given application of GEBs. In this paper, we fill this gap by providing an extensive survey of the literature on system modeling for different GEBs that have been produced in the past decade. This survey involves over 300 relevant journal articles from the building community, the power system community, and the society of control. Specifically, we first identified key requirements of system modeling for GEBs based on various types of applications discussed in those publications. We then summarized the system modeling applications from those publications, in terms of their assumptions, modeling approach, and simulation. After that, we analyzed different assumptions made for various applications, the extent to which those modeling approaches satisfied the key requirements, and how those approaches can be scaled up for large-scale applications, which are quite common in GEBs. In addition, we gave insights on how to use system modeling for different applications in GEBs. At the end, we provided recommended directions for future studies to fully unleash the potentials of system modeling to support GEBs.

Recent research has shown the energy-saving potential of occupancy-based HVAC controls (OBCs) in commercial buildings. However, building energy codes have not fully adopted this technology. This study aims to evaluate the cost-effectiveness and decarbonization benefits of OBCs and provide guidance for integrating occupancy sensors into building energy code development. To this end, a parametric simulation using EnergyPlus and a nationwide cost-effectiveness analysis are carried out considering three building types and 40 representative cities in the U.S. The findings reveal that the current cost-effectiveness performance of OBCs is limited due to the high cost of occupancy sensors. However, incorporating the societal cost of carbon factor in future energy and environmental policy could greatly enhance the actual cost-effectiveness performance. Besides, a reduction in the cost of occupancy sensors to approximately 60% of the current price level could also greatly shorten the dis-counted payback period of OBCs. Additionally, OBCs demonstrate significant potential in building decarbon-ization, with potential CO2 emissions savings of more than 5.56 million metric tons across the three building types and 40 selected cities. Finally, policy implications are provided to guide the incorporation of occupancy-based HVAC controls in future energy codes.

The United States building sector consumed approximately 75% of electricity in 2019. By implementing renewable energy technologies and control strategies into buildings, future buildings will serve as energy generators as well as consumers. To accommodate this transition, communications among buildings and between buildings and the grid could provide more possibilities to optimize the energy performance of buildings. This paper develops a community-scale building energy model tool and conducts a case study adopting behind-the-meter distributed energy resources, sharing energy in different buildings, and using different electricity tariff structures. Three scenarios are studied: (1) electricity only supplied by the grid, (2) photovoltaic (PV) panels installed on and available to some but not all buildings, and (3) a connected community. To consider the impacts of locations and energy tariffs, this paper selects four cities and three electricity tariffs to evaluate the energy and cost performances of these three scenarios. The results show that the PV panels in Scenario 2 reduce 25% to 33% of the community-level electricity consumption and 20% to 30% of the community-level electricity cost compared with Scenario 1 in all studied locations and energy tariffs. By considering power management in the connected community (Scenario 3), the electricity consumption and cost can be further reduced by 6% to 7% and 5% to 11%, respectively, compared with Scenario 2.

Homes built before 1992, when the U.S. Department of Energy’s (DOE) Building Energy Codes Program was established, represent approximately 68% of the residential building stock in the country . Up to 43% of these homes have little to no insulation in the walls and have very high air leakage rates of 10 or more air changes per hour at 50 pascals of pressure (ACH50). These issues can represent a substantial portion of unnecessary money spent on utility bills for homeowners, especially in the colder climates. There is a significant need for cost-effective, reliable retrofit methods for these homes that include air, moisture, and vapor controls which are considered best practices for high-performance new home construction. Well-tested and documented wall retrofit systems can help to achieve substantial energy savings and also improve durability, comfort, health, and resilience. In 2018, DOE’s Building Technologies Office awarded Pacific Northwest National Laboratory, Oak Ridge National Laboratory, and the University of Minnesota funding to complete a 3-year project to compare a range of residential wall retrofit systems that prioritized affordability, durability and energy savings potential. In addition to these core criteria, the ease-of-construction and the wide-scale applicability of the solutions also were considered. In this project, the research team identified, constructed, tested, simulated, and analyzed the feasibility and economics of 16 wall retrofit assemblies (14 test configurations and two baseline configurations).

Occupancy-based control (OBC) in smart buildings provides numerous potentials to improve building energy efficiency. To achieve effective OBC, the occupancy status of a space or a building needs to be well understood in terms of whether the space is occupied and how many people are in the space. Well-designed and selected people counting technologies are cornerstones for OBC. However, the study and application of such technologies in OBC are relatively new. There are few mature people counting sensors available on the market. In addition, there is a lack of standardized guidance to comprehensively test, evaluate and compare the performance of people counting sensors, which further limits the selection and application of them in OBC. Hence, an innovative testing protocol was developed to fill in the gap. This paper introduces the design of the protocol with eight diversities and discusses case studies that evaluated four representative types of people counting sensors following the proposed protocol. It is found that the protocol can effectively guide a comprehensive evaluation of the selected sensors and lead to informative findings on the sensor performance. The test results can provide insights and suggestions not only for sensor developers to improve the sensor hardware and software design but also for heating, ventilation, and air-conditioning (HVAC) system designers and building managers to select the proper people counting sensors for OBC design. The test results on the sensor accuracy can also support additional evaluations of integrated OBC and building system operation. It is anticipated that the developed methodology can offer guidance on the test and evaluation of other occupancy sensing technologies.

The recognition of the determinant role of occupants in building energy consumption has catalyzed the increasing research activities on occupancy-centric controls (OCCs) for building operations. This paper evaluates the monetary savings of the occupancy-centric heating, ventilation, and air-conditioning (HVAC) controls in four types of commercial buildings, i.e., medium office, large office, hotel, and secondary school. A parametric EnergyPlus simulations were conducted to calculate the energy savings from the occupancy-centric HVAC controls in different climate zones, based on assumptions on occupancy. The data of energy price, labor price, sensor cost, and parameters for the life-cycle cost analysis (LCCA) are collected from various validated sources such as the U.S. Energy Information Administration (EIA) and Bureau of Labor Statistics (BLS), to support the economic analysis. Three evaluation metrics, i. e., net savings (NS), savings-to-investment ratio (SIR), and discounted payback period (DPB), are selected to assess the economic performance. The results-suggest that based on the current sensor price and assumptions of occupancy schedules, the typical DPB is longer than 10 years (i.e., the typical lifespan of a sensing system) in half of the representative cities for the medium office, and in nearly all representative cities for the large office building. This indicates that the investments on sensor systems are not likely to be covered by the long-term energy cost savings in such office building type. Nevertheless, the large hotel and primary school buildings are good candidates for occupancy sensors: the large hotel could achieve a DPB shorter than two years for all scenarios, while the primary school could achieve a DPB shorter than five years for most of the representative cities.

Thermostat management plays a significant role in household energy conservation. This study aims to conduct a systematic and comprehensive analysis to quantify the energy savings potential of the occupant-centric smart thermostat based on a large-scale nationwide simulation infrastructure. The single-family Residential Prototype Building Model was used to represent a typical single-family detached house in the U.S. A generalized random occupancy presence schedule was created based on an occupancy probability schedule and k-means clustering algorithm. A total of 16,000 simulations, which were composed of four building foundation types, four heating source types, 40 American cities, five building energy code versions, and five thermostat control strategies, were conducted to evaluate the performances of the smart home thermostat in terms of saving building energy usage and maintaining occupant thermal comfort. The nationwide simulation results suggested that the temperature setback control during the unoccupied period could achieve some energy savings in the U.S. households. However, only very few of the 40 cities could see an annual Heating, Ventilation, and Air-conditioning energy savings ratio of over 30%. Besides, the implementation of the occupied standby temperature reset could greatly increase the peak load of the HVAC system and contribute to the grid load imbalance issue. It’s also worth noting that the smart recovery feature is proved to be able to bring additional benefits for a smart home thermostat. It could decrease the temperature setpoint not met time by about 30 min, and relieve the thermal discomfort due to the temperature setback control.

Quantifying the energy savings of various energy efficiency measures (EEMs) for an energy retrofit project often necessitates an energy audit and detailed whole building energy modeling to evaluate the EEMs; however, this is often cost-prohibitive for small and medium buildings. In order to provide a defined guideline for projects with assumed common baseline characteristics, this paper applies a sensitivity analysis method to evaluate the impact of individual EEMs and groups these into packages to produce deep energy savings for a sample prototype medium office building across 15 climate zones in the United States. We start with one baseline model for each climate zone and nine candidate EEMs with a range of efficiency levels for each EEM. Three energy performance indicators (EPIs) are defined, which are annual electricity use intensity, annual natural gas use intensity, and annual energy cost. Then, a Standard Regression Coefficient (SRC) sensitivity analysis method is applied to determine the sensitivity of each EEM with respect to the three EPIs, and the relative sensitivity of all EEMs are calculated to evaluate their energy impacts. For the selected range of efficiency levels, the results indicate that the EEMs with higher energy impacts (i.e., higher sensitivity) in most climate zones are high-performance windows, reduced interior lighting power, and reduced interior plug and process loads. However, the sensitivity of the EEMs also vary by climate zone and EPI; for example, improved opaque envelope insulation and efficiency of cooling and heating systems are found to have a high energy impact in cold and hot climates.