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This report serves as a resource for building owners and managers interested in deploying GEB technologies in federal and commercial facilities. This document also provides an overview of smart buildings and the prioritization and categorization of GEB technologies that have a high potential to provide grid services.
Frequent heatwaves and hot summers increasingly challenge occupant comfort, health, and energy grid stability. Addressing these challenges requires a detailed understanding of household cooling behaviors, such as thermostat adjustments and adaptive responses to extreme conditions. Traditional analyses often rely on aggregated numerical metrics that overlook subtle but important household-specific variations. In this study, we introduce a generalizable methodology that integrates large language models (LLMs) with vision capabilities to enable scalable and detailed analysis of residential thermostat data. Using Ecobee's Donate Your Data (DYD) dataset—which provides five-minute records of indoor temperatures, thermostat setpoints, and HVAC runtimes—we focus on two U.S. cities with contrasting summer climates : Austin (TX) and Phoenix (AZ). Because raw time-series data are not well suited for direct LLM analysis, we transform them into visual representations, such as daily indoor temperature trajectories and weekly runtime histograms, to better capture behavioral variations. Leveraging LLMs' visual interpretation, we extract descriptive behavioral features, including temperature preferences, time-of-day cooling orientation, anticipatory versus reactive heatwave responses, and behavioral consistency. These semantic features support unsupervised clustering to identify distinct occupant archetypes at scale, revealing differences—such as morning-centric anticipatory coolers versus households that shift toward warmer setpoints during heatwaves—that can inform demand response, resilience planning, and health-aware interventions. By converting raw numerical data into interpretable behavioral patterns, this methodology enables scalable and practical analysis of occupant behavior, supporting actionable insights for comfort, resilience, and energy management.
This issue of the ASME Journal of Engineering for Sustainable Buildings and Cities (JESBC) is fully dedicated to peer-reviewed papers specific to technologies and applications of Grid-interactive Efficient Buildings (GEBs). As defined by a series of publications by the U.S. Department of Energy (DOE) and outlined in Part 1 of the editorial of JESBC August 2020 issue, GEBs are efficient, connected, smart, and flexible buildings. This GEB special issue covers some technologies and applications for grid-connected buildings and communities. Some of technologies included in the special issue are dynamic insulation systems for building envelopes, building integrated fuel cells, and rooftop PV systems. Furthermore, the issue outlines the designs and cost-benefits of net-zero energy (NZE) buildings and carbon neutral communities in the United States.
Building electrical load profiles can improve understanding of building energy efficiency, demand flexibility, and building-grid interactions. Current approaches to generating load profiles are time-consuming and not capable of reflecting the dynamic and stochastic behaviors of real buildings; some approaches also trigger data privacy concerns. In this study, we proposed a novel approach for generating realistic electrical load profiles of buildings through the Generative Adversarial Network (GAN), a machine learning technique that is capable of revealing an unknown probability distribution purely from data. The proposed approach has three main steps: (1) normalizing the daily 24-hour load profiles, (2) clustering the daily load profiles with the k-means algorithm, and (3) using GAN to generate daily load profiles for each cluster. The approach was tested with an open-source database – the Building Data Genome Project. We validated the proposed method by comparing the mean, standard deviation, and distribution of key parameters of the generated load profiles with those of the real ones. The KL divergence of the generated and real load profiles are within 0.3 for majority of parameters and clusters. Additionally, results showed the load profiles generated by GAN can capture not only the general trend but also the random variations of the actual electrical loads in buildings. We report the proposed GAN approach can be used to generate building electrical load profiles, verify other load profile generation models, detect changes to load profiles, and more importantly, anonymize smart meter data for sharing, to support research and applications of grid-interactive efficient buildings.
Emerging grid-interactive efficient buildings (GEBs) have great potential to provide much-needed demand flexibility to electric grids while fulfilling their own control targets by co-optimizing smart appliances, solar photovoltaics, electric vehicles, and energy storage at buildings. To enable the optimal operation of GEBs, site-specific weather information—such as temperature, solar irradiance, relative humidity, and wind speed—is crucial; however, this information is generally unavailable or expensive to obtain. This paper develops advanced machine learning methods to provide precise weather forecasts for individual building sites using readily available weather station data. Support vector regression and artificial neural networks have been employed to learn the spatiotemporal correlations between the weather conditions at nearby weather stations and the individual building site. The proposed site-specific weather forecasting methods have been validated using 1-year actual weather measurement data collected in the Denver metro area. Results show that the developed machine-learning-driven methods can accurately forecast the temperature at the target building site 1 hour ahead with mean absolute error less than 0.72°C and a 48% improvement over the persistence method. Site-specific weather forecasts will improve the understanding of the microclimate effect and its impact on building energy consumption. This information will drive efficiency upgrades and adjustments of building control strategies to improve energy savings and increase flexibility in building loads.
The combination of increased electric load growth across all sectors, deferred electrical infrastructure investment, and other factors resulting in variable electric power supply, has created technical challenges to maintaining a resilient and reliable grid. Many federal, regional, and local efforts are in play to modernize the electric grid, including advancing building technologies and distributed energy resources (DERs) that are utilizing smarter controls to become responsive to both occupant and grid needs. This report reviews ten pilot projects demonstrating how groups of buildings combined with behind-the-meter (BTM) DERs such as electric vehicle (EV) charging, battery storage, flexible HVAC and domestic hot water systems, and photovoltaic systems can reliably and cost effectively provide grid services. Each of the ten pilot projects aim to deliver both energy efficiency and demand flexibility (DF) while supporting load growth. The ten demonstration teams are piloting flexible DER packages across diverse communities of residential and commercial buildings to address a variety of regional grid needs. The outcomes of these pilot projects will be used to inform future scaling through utility program development. This paper characterizes the ten teams, showcasing the decision-making process used by each group to develop their packages (Section 2), the grid services they plan to deliver (Section 3), the types of DER packages selected for deployment within building sectors (Section 4) and trends between building sector, DER types, and grid services In order to achieve community scale benefits, the pilot projects must utilize aggregated control mechanisms for coordinating buildings and DERs together. Several types of coordinated control architectures have evolved amongst the teams, influenced by use type, existing market conditions, and integration type. Three coordinated controls architectures have been characterized, highlighting their use cases, benefits, challenges, and tradeoffs in their design. These insights can aid utilities, control vendors, and developers in scaling community-level energy systems (Paul, 2024). Ultimately, the technology packages selected by the ten teams will be coordinated to provide power system services, also known as grid services. Insights from these demonstrations will be useful for grid operators, regulators, aggregators and other stakeholders as they look to deploy demand flexible resources as grid services in the future. The grid services that each team is targeting for demonstration are described in Section 3 and Section 4. Methods for evaluating the grid services have been described in the paper Metrics for Evaluating Grid Service Provision from Communities of Grid-interactive and Efficient Buildings and other DER (MacDonald, 2023). To identify technology packages for demonstration, Section 2 shows that project teams used a range of analysis approaches, including building energy modeling, AMI data analysis, cost-benefit frameworks, and utility pilot data. Some teams emphasized technical modeling to quantify grid impacts and demand reduction potential, while others prioritized economic evaluations, stakeholder input, or exploratory pilots to inform deployment decisions. This diversity reflects the need to tailor selection methods to project goals, available data, and organizational context. Section 5 discusses trends between the DER technologies deployed and the grid service provisions from each team. Residential buildings (multifamily and single family) lean towards technologies that enhance energy efficiency (e.g. weatherization upgrades, smart thermostats) and onsite power generation integration (e.g. solar PV). Commercial building demonstrations prioritize technologies that ensure operational reliability (e.g. battery storage) and centralized energy management systems and optimization solutions. Teams that are deploying controllable storage-based technologies are more likely to provide grid services that require a near real-time response. Teams incorporating load shifting technologies like smart thermostats with HEMs are likely to include energy markets participation and customer bill management offerings. Campus demonstrations are adopting diverse sets of DERs to emphasize renewable generation, paired with centralized control. This section also describes technologies that were considered during project planning but ultimately excluded from final deployment. These demonstrations reveal that effective DER package design should be tailored to building type, customer segment, and construction vintage. Multifamily buildings benefit from centralized HVAC upgrades and supervisory controls, while single-family homes are well-suited for individualized technologies like solar, storage, and smart home energy monitors. Commercial and campus settings prioritize EMIS integration and load optimization. New construction enables cost-effective integration of DER-ready infrastructure, whereas retrofits require deployments aligned with owner and tenant value streams. For utility program planners, early coordination with developers and building owners, paired with segmented and modular program offerings, can improve adoption, scalability, and grid impact.
The electricity landscape is evolving towards more decentralized approaches due to the proliferation of distributed energy resources and the participation of increasingly smart consumers and producers (prosumers). Recent advances in information and communication technologies and smart metering, provides strategic opportunities for “prosumers” to reform their conventional energy practices towards more consumer-centric economies. From an operational perspective, managing power distribution networks is becoming more difficult with such active grid-edge systems providing limited to no visibility or control. Transactive Energy (TE) has been emerging as a key enabler towards effectively and efficiently integrating prosumers into competitive electricity markets. This work presents a transactive implementation of community-centric markets. A co-simulation framework is developed for evaluating the proposed market structure with high-fidelity models. Case studies on the IEEE-123 node test system demonstrate that community-centric transactive markets can enable communities of prosumers to operate collaboratively as grid-edge systems. The potential benefits of implementing community-centric TE systems are also illustrated.
The purpose of this smart buildings case study is to showcase a leading example of a GEB renovation project in the federal buildings space and provide key information on the technology and control upgrades, costs, and energy and utility bill savings. This case study also provides information and recommendations for selecting energy conservation measures (ECMs) and choosing energy and cost reduction strategies from the energy management team at the site. The findings from this successful GEB project can be used to help pave the way for additional GEB retrofits in the future. The Carl T. Hayden Veterans Affairs (VA) Medical Center in Phoenix, Arizona, demonstrates that GEB strategies and technologies can be realistically deployed today across buildings with substantial energy and cost savings. The project implemented both ECMs and grid-interactive technologies and controls strategies, making it a leading example of a smart, sustainable, and efficient commercial building.
The high penetration of distributed energy resources (DERs) in active distribution systems has posed challenges to the centralized distribution system restoration (DSR) strategies in current practice. On the other hand, the advancement in smart inverter technologies enables the bottom-up restoration capability. This paper is motivated to develop a 3-layered hierarchical framework for decentralized DSR, based on the grid-forming (GFM) and grid-following (GFL) grid-edge inverters. The first layer presents the tertiary control, which determines the load pickup schedule and generation dispatch of DERs, using the alternating direction method of the multipliers algorithm. The second layer consists of two control functions: GFM control, which regulates voltage and frequency, establishing a stable grid for GFL inverters to follow; and GFL control, which regulates the real and reactive power. In the third layer, the primary control is proposed to regulate the inverter voltage and current, which is developed based on the virtual oscillator control (VOC). Furthermore, the developed framework is tested in the modified IEEE 13-node test feeder. Two scenarios of grid-connected and islanded operating modes are designed, and simulation results demonstrate the effectiveness of decentralized DSR strategies for controlling grid-edge inverters to enhance the distribution system resilience.
Fast-growing freight activities over the decades have become one of the major contributors to air pollution, leading to many efforts in freight decarbonization and electrification. However, the development of freight electrification is slow due to technological uncertainty, slow charging, high capital cost, etc. This paper analyzes the potential impact and benefit of heavy-duty vehicle (HDV) electrification and automation on fleet cost, infrastructure cost, the electricity grid, and environmental outcomes. In this work, we extended the vehicle electrification benefit analysis tool: Grid-Electrified Mobility (GEM) model, which had primarily been used to study light-duty passenger vehicles (LDVs), to analyze heavy-duty vehicle electrification. The extended model is derived for freight transportation electrification, and different freight electrification and automation adoption scenarios were analyzed. We find that the increased penetration of automated electric freight fleets within other types of electrified freight fleets from 1% to 99% will result in an overall cost reduction of 18.2%, fleet size reduction of 20.4%, and lower peak load reduction of 14.3%.
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The project’s overall objective is to research, develop, and conduct a wide-scale demonstration of a utility smart charge management (SCM) system to develop optimal managed charging structures for grid value, evaluate the impact of EV charging on local distribution utility operations, and evaluate the utilities’ ability to control electronic vehicle (EV) charging load based on grid conditions. This report covers Argonne’s validation testing in the first period of the project, the objective of which was to determine the current managed charging functionality of the EVSE hardware, the network and the vehicle telematics software solution and understand gaps that need to be addressed prior to a large-scale demonstration.
Electric vehicles (EVs) are expected to drastically increase residential electricity consumption and could provide a significant source of flexible demand. Aggregating smart EV charge controllers with other smart home devices through a home energy management system can lead to more optimal outcomes that benefit homeowners, utilities, and grid operators. Control strategies should consider occupant convenience by accounting for the need for fully charged EVs near the EV departure time. In this paper, we develop an EV charging framework that accounts for occupant convenience using OCHRE, a residential energy model, and foresee, a home energy management system. We simulate a community with high EV penetration and show that integrated, smart EV charging reduces peak demand and smooths night-time energy consumption. Simulation results show that the proposed control strategy nearly eliminates peak period EV charging and reduces the daily peak demand from EVs by 23%.
The building sector is one of the largest contributors to the world’s total energy use and greenhouse gas emissions. Advancements in building energy technologies have played a critical role in enhancing the energy sustainability of the built environment. Extensive research and new techniques in energy and environmental systems for buildings have recently emerged to address the global challenges. This study reviews existing articles in the literature, mostly since 2000, to explore technological advancement in building energy and environmental systems that can be applied to smart homes and buildings. This review study focuses on an overview of the design and implementation of energy-related smart building technologies, including energy management systems, renewable energy applications, and current advanced smart technologies for optimal function and energy-efficient performance. To review the advancement in building energy-related technologies, a systematic review process is adopted based on available published reviews and research types of articles. Review-type articles are first assessed to explore the current literature on the relevant keywords and to capture major research scopes. Research-type papers are then examined to investigate associated keywords and work scopes, including objectives, focuses, limitations, and future needs. Throughout the comprehensive literature review, this study identifies various techniques of smart home/building applications that have provided detailed solutions or guidelines in different applications to enhance the quality of people’s daily activities and the sustainability of the built environmental system. This paper shows trends in human activities and technology advancements in digital solutions with energy management systems and practical designs. Understanding the overall energy flow between a building and its environmentally connected systems is also important for future buildings and community levels. This paper assists in understanding the pathway toward future smart homes/buildings and their technologies for researchers in related research fields.
Abstract With climate change, heat waves have become more frequent and intense. Rotating power outages happen when the power supply is unable to meet the cooling demand increase resulting from extreme high temperatures. Power outages during heat waves expose residents to high risks of overheating. In this study, we propose a novel data-driven inverse modelling approach to inform decision makers and grid operators on planning rotating power outages. We first infer the building thermal characteristics using the connected smart thermostat data, and used the estimated thermal dynamics to simulate the thermal resilience during a heat wave event. Our proposed method was tested for the California power outage in August 2020 by using the open source Ecobee Donate Your Data dataset. We found in California the power outage should not last more than two hours during heat waves to avoid overheating risks. Informing the residents in advance so they can prepare for it through pre-cooling is a simple but effective strategy to expand the acceptable power outage duration. In addition to assisting power outage planning, the proposed method can be used for other applications, such as to evaluate a building energy efficiency policy, to examine fuel poverty, and to estimate the load shifting potential of building stocks.
The growth of and reliance on renewable energy necessitate a multi-pronged approach to achieve grid reliability and economics. As they represent a notable portion of U.S. energy consumption, commercial buildings must play an active role in this effort. Conserving energy and responding to grid conditions through demand flexibility can be achieved through the integration of major building systems. Integration of plug and process loads with lighting and heating, ventilation, and air conditioning systems maximizes the effectiveness of integrated building energy management. In this research, we demonstrate the integration of smart outlets into a building automation system. We cover the installation process as well as the architecture required for smart outlets to communicate data to the building automation system and to receive commands back. After recording power measurements for one week as a baseline, we configured the building automation system to turn the smart outlets on and off according to a set schedule. This resulted in energy savings of 66% during 1 week on 25 plug loads. This work demonstrates that grid-interactive efficient buildings are achievable through building system integration.
Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.
Increasing integration of distributed solar photovoltaic (PV) into distribution networks could result in adverse effects on grid operation. Traditional model-based control algorithms require accurate model information that is difficult to acquire and thus are challenging to implement in practice. Here, this paper proposes a surrogate model-enabled grid visibility scheme to empower deep reinforcement learning (DRL) approach for distribution network voltage regulation using PV inverters with minimal system knowledge. In contrast to existing DRL methods, this paper presents and corroborates the adverse impact of missing load information on DRL performance and, based on this finding, proposes a surrogate model methodology to impute load information utilizing observable data. Additionally, a multi-fidelity neural network is utilized to construct the DRL training environment, chosen for its efficient data utilization and enhanced robustness to data uncertainty. The feasibility and effectiveness of the proposed algorithm are assessed by considering DRL testing across varying degrees of observable load information and diverse training environments on a realistic power system.