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The Department of Energy's (DOE) Office of Electricity (OE) sponsored the "Frontiers in Energy Storage: Next-Generation Artificial Intelligence (AI) Workshop", which was hosted at Lawrence Berkeley National Laboratory on April 16, 2024. This hybrid event convened industry leaders, researchers, and innovators both in-person and virtually to discuss the transformative potential of AI in enhancing the development and adoption of grid-scale energy storage. Participants presented and discussed how advancements in machine learning (ML) and AI can catalyze innovation in material development, system integration and optimization, performance validation, and strategic policy development.The wide-ranging workshop spanned topics from accelerated materials development to policy and valuation of long duration energy storage systems as well as the use of AI-powered agentic systems to manage grid operations. Throughout the event, participants highlighted the technical, social, and financial hurdles in building and maintaining robust data infrastructures that serve as the foundation for AI/ML innovation. Key recommendations were made across the entire range of topics, with clear needs for the development of scalable and trustable AI tools, enhancement of data availability and interoperability, the need for interpretability and transparency in policy decisions, and promotion of cross-sector collaboration to realize the full potential of AI in energy storage. Additionally, the workshop underscored the importance of fostering collaboration between academic/government researchers and industry to fully unlock the potential of AI in energy storage and grid operations.This report summarizes these discussions, with the goal to guide and inform future advancements of AI for energy storage that align with national goals for energy efficiency and sustainability.

In response to increasingly severe weather conditions, optimization of building performance and investment provides an opportunity to consider co-benefits of thermal resilience during energy efficiency retrofits. This work aims to assess thermal resilience of buildings using building performance simulation to evaluate the indoor overheating risk under nine weather scenarios, considering historical (2010s), mid-term future (2050s), and long-term future (2090s) typical meteorological years, and heat wave years. Such an analysis is based on resilience profiles that combine six integrated indicators. A case study with a district of 92 buildings in Brazil was conducted, and a combination of strategies to improve thermal resilience was identified. Results reflect the necessity of planning for resilience in the context of climate change. This is because strategies recommended under current conditions might not be ideal in the future. Therefore, an adaptable design should be prioritized. Cooling energy consumption could increase by 48 % by the 2050s, while excessive overheating issues could reach 37 % of the buildings. Simple passive strategies can significantly reduce the heat stress. A comprehensive thermal resilience analysis should ultimately be accompanied by a thorough reflection on the stakeholders’ objectives, available resources, and planning horizon, as well as the risks assumed for not being resilient.

Building energy flexibility plays a critical role in demand-side management for reducing utility costs for building owners and sustainable, reliable, and smart grids. Realizing building energy flexibility in tropical regions requires solar photovoltaics and energy storage systems. However, quantifying the energy flexibility of buildings utilizing such technologies in tropical regions has yet to be explored, and a robust control sequence is needed for this scenario. Hence, this work presents a case study to evaluate the building energy flexibility controls and operations of a net-zero energy office building in Singapore. The case study utilizes a data-driven energy flexibility quantification workflow and employs a novel data-driven model predictive control (MPC) framework based on the physically consistent neural network (PCNN) model to optimize the building energy flexibility. To the best of our knowledge, this is the first instance that PCNN is applied to a mathematical MPC setting, and the stability of the system is formally proved. Three scenarios are evaluated and compared: the default regulated flat tariff, a real-time pricing mechanism, and an on-site battery energy storage system (BESS). Our findings indicate that incorporating real-time pricing into the MPC framework could be more beneficial to leverage building energy flexibility for control decisions than the flat-rate approach. Moreover, adding BESS to the on-site PV generation improved the building self-sufficiency and the PV self-consumption by 17% and 20%, respectively. This integration also addresses model mismatch issues within the MPC framework, thus ensuring a more reliable local energy supply. Future research can leverage the proposed PCNN-MPC framework for different data-driven energy flexibility quantification types.

Anthropogenic heat (AH), i.e., waste heat from buildings to the ambient environment, increases urban air temperature and contributes to the urban heat island effect, which leads to more air-conditioning energy use and higher associated waste heat during summer, forming a positive feedback loop. This study used a bottom-up simulation approach to develop a dataset of the annual hourly AH profiles for 1.7 million buildings in Los Angeles (LA) County for the year 2018 aggregated at three spatial resolutions: 450 m, 12 km, and the census tract. Building AH exhibits strong seasonal and diurnal patterns, as well as large spatial variations across the urban areas. Building AH peaks in May and reaches a maximum of 878 W/m2 within one of several AH hotspots in the region. Among the three major AH components (surface convection, heat rejection from HVAC systems, and zonal air exchange), the surface convection component is the largest, accounting for 78% of the total building AH across LA County. Higher AH is attributed to large building density, a high percentage of industrial buildings, and older building stock. While AH peaks during the day, the resulting ambient temperature increases are much larger during the night. During the July 2018 heatwave in LA County, building AH (excluding the surface component) leads to a daily maximum ambient temperature increase of up to 0.6 °C and a daily minimum ambient temperature increase of up to 2.9 °C. It is recommended that reducing summer building AH should be considered by policy makers in developing mitigation measures for cities to transition to clean energy while improving heat resilience.

When renewing building facades in most Chinese cities, covers are often designed and installed to conceal the outdoor units of residential split air conditioners for congruousness of building facades. However, how those covers impact airflow and energy efficiency should be thoroughly examined. This study investigates two popular designs of cover (louver and circular-hole) and selects Nanjing, renowned for its hot summers, as the case city. This study employs Computational Fluid Dynamics models to simulate airflow obstruction caused by the covers surrounding the outdoor units. Grey Model, trained from the CFD results, quantifies interactions between design of cover and inlet air temperature. Additionally, a thermodynamic model assesses the covers' impact on the coefficient of performance of air conditioners. The findings reveal that the primary cause of outdoor unit overheating is warm air backflow from the outlet to the inlet. A mere 1 °C increase in inlet air temperature can reduce COP by 4.1%–8.7%. The maximum temperature differences caused by different designs of cover are 4.5 °C for the louver type and 4.4 °C for the circular-hole type. To mitigate these negative impacts, optimal design recommendations are provided: a minimum distance between the cover and outdoor unit of 0.29 m for louver type and 0.1 m for circular-hole type, both feasible in most installations. These findings can help designers, manufacturers, installers and policymakers, to facilitate the design and installation of optimal covers for outdoor air-conditioning units, thus minimizing energy efficiency losses.

Los Angeles County has been experiencing increasing numbers of very hot days and heatwaves later into the summer. Scientists predict a significant increase in days with temperatures over 95 °F [35 °C] over the next 15-20 years. As the climate warms, deploying energy efficiency technologies at scale can be an effective method to address the associated increase in urban temperatures, particularly during periodic heatwaves.Utilizing existing information and data, the research project provided the Los Angeles County Internal Services Department with supplementary datasets and tools that support the identification and prioritization of neighborhoods/districts, building clusters, and specific buildings for retrofits, according to criteria identified and developed during the analysis process. There was a focus on the County’s multi-family residential building stock, to enable low-cost energy retrofits that also support improvement of thermal resilience and occupant health and comfort.Urban fabric analysis indicated that there are substantial opportunities to reduce unwanted solar heating of buildings and pavements and cool the urban environment with reflective roofs, walls, and pavements. On average, albedo (solar reflectance) values for roofs (0.19) and pavements (0.15) are currently quite low. The potential increases to roof and pavement albedos are 0.33, and 0.25 respectively, indicating substantial opportunities for brightening. Building stock analysis determined that there are over 43,000 multi-family residential buildings, holding over 150,000 housing units in Los Angeles County, that represent priority opportunities for large-scale energy efficiency policies and activities to ameliorate the impacts of the urban heat island effect.Building energy simulations were conducted for 13 multifamily residential buildings, to evaluate a low-cost energy efficiency retrofit package. Implementation of these measures in the baseline housing units are expected to reduce annual electricity use by 17%, lower peak electricity demand by 19%, and provide net annual energy cost savings of $183 per housing unit. Importantly, annual hours of heat stress for building occupants are expected to be dramatically reduced by implementation of the energy efficiency retrofit—and for buildings with existing air-conditioning, eliminated entirely.Unmanned Aerial Vehicle (UAV) flights were conducted as a remote-sensing proof-of-concept for building energy performance evaluation. This approach represents an opportunity for inspection and analysis of existing building stocks, where energy performance can be documented using infrared imaging and 3D photogrammetry. Machine-learning algorithms developed by Berkeley Lab researchers were employed to detect thermal anomalies on building facades, and to indicate issues potentially related to moisture, infiltration, and/or exfiltration. The potential benefits can be seen via implementation of this method at the building test site, which highlighted areas around windows and facade surface corners as locations for further inspection.

Deep Reinforcement Learning (DRL)-based control shows enhanced performance in the management of integrated energy systems when compared with Rule-Based Controllers (RBCs), but it still lacks scalability and generalisation due to the necessity of using tailored models for the training process. Transfer Learning (TL) is a potential solution to address this limitation. However, existing TL applications in building control have been mostly tested among buildings with similar features, not addressing the need to scale up advanced control in real-world scenarios with diverse energy systems. This paper assesses the performance of an online heterogeneous TL strategy, comparing it with RBC and offline and online DRL controllers in a simulation setup using EnergyPlus and Python. The study tests the transfer in both transductive and inductive settings of a DRL policy designed to manage a chiller coupled with a Thermal Energy Storage (TES). The control policy is pre-trained on a source building and transferred to various target buildings characterised by an integrated energy system including photovoltaic and battery energy storage systems, different building envelope features, occupancy schedule and boundary conditions (e.g., weather and price signal). The TL approach incorporates model slicing, imitation learning and fine-tuning to handle diverse state spaces and reward functions between source and target buildings. Results show that the proposed methodology leads to a reduction of 10% in electricity cost and between 10% and 40% in the mean value of the daily average temperature violation rate compared to RBC and online DRL controllers. Moreover, online TL maximises self-sufficiency and self-consumption by 9% and 11% with respect to RBC. Conversely, online TL achieves worse performance compared to offline DRL in either transductive or inductive settings. However, offline Deep Reinforcement Learning (DRL) agents should be trained at least for 15 episodes to reach the same level of performance as the online TL. Therefore, the proposed online TL methodology is effective, completely model-free and it can be directly implemented in real buildings with satisfying performance.

The compound impacts of heatwaves and power outages pose a serious indoor heat-related health risk for residents living in disadvantaged communities (DACs) with limited or no air conditioning. In this study we selected 13 heat vulnerable multifamily buildings in El Monte, in Los Angeles County, and employed CityBES to evaluate their energy and thermal resilience performance. A retrofit package with seven passive and low-power active measures—cool roof, cool wall, window solar film, air sealing, internal blinds, natural ventilation, and ceiling fan—was evaluated under 2018 weather conditions and projected 2058 future weather conditions. Results show: (1) under the 2018 weather conditions, the retrofit package reduces the peak electricity load by 19 % and reduces the annual energy cost by $183 per housing unit; (2) the housing units without air conditioning would face heat danger conditions throughout the heatwave period. Although the retrofit package could reduce the heat danger hours by 50 % in 2018 and 34 % in 2058, air conditioning is a life-essential need for residents during heatwaves. These results indicate that, during the decision making of energy and climate retrofits for housing in DACs, policymakers and building owners should consider the co-benefits of reducing indoor heat-related mortality while reducing energy cost.

Understanding thermal dynamics and obtaining the computational model of residential buildings enable its scaled application in energy retrofits, control optimization and decarbonization. In this paper, we present a deep learning approach to model building thermal dynamics with smart thermostat data collected from residential buildings, with the goal to investigate model generalizability. In the first stage, we developed and compared different Deep Learning architectures including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) models and CNN-LSTM to predict indoor air temperature in a multi-step time horizon. In the second stage, we implemented a Transfer Learning (TL) process, which aims to improve the prediction performance on a new set of buildings (targets), exploiting the knowledge of related or similar buildings (sources). Different TL strategies and source model identification methods were investigated. The study showed that the CNN-LSTM performed the best among the architectures compared, with an average Mean Absolute Error (MAE) of 0.26 °C for one-hour-ahead (twelve 5-min future steps) predictions. Furthermore, the results showed that freezing the LSTM layer and fine-tuning the other layers of the CNN-LSTM achieved the best performance among four TL strategies, which further improved the performance with respect to a machine learning approach by 10%, and proving the effectiveness and generalizability of the proposed approach. A comparison of three different source model identification methods showed that randomly selecting source models constrained by similar building characteristics can provide good TL performance while retaining simplicity comparing with other quantitative source identification methods.

We need human behavioural change to decarbonize our buildings. This requirement arises from our needs, lifestyle energy choices and interactions with buildings, and is an underexploited, yet essential demand-side opportunity for rapid and sustainable decarbonization. We propose a sufficiency-oriented approach that fosters equitable building decarbonization, while maintaining planetary boundaries.

BayREN Integrated Commercial Retrofits (BRICR) is a DOE-funded project which aims to enhance the capacity of energy efficiency programs to recruit participants, develop retrofits, and measure outcomes in small and medium-sized commercial buildings, a sector notoriously hard to reach and expensive to serve, that accounts for ⅔ of US commercial floor space. To address these barriers, BRICR leverages existing incentives, financing, data, and open source software to facilitate two paths for comprehensive improvements: a deep energy retrofit, or serial upgrades integrated into capital improvement and maintenance cycles. BRICR is developing an integrated workflow for iterative energy modeling of commercial buildings for city energy program managers and auditors - starting with mass building-scale simulation based on public records and proceeding through audit, retrofit, and measurement and verification stages. BRICR builds on existing tools including LBNL’s CBES, NREL’s OpenStudio, PNNL’s Audit Template, and DOE’s BuildingSync and SEED PlatformTM. At each stage, BRICR uses available information to inform simulations (starting with public records but iteratively augmented with observations from energy program staff) to improve the quality of the models that inform decision making. This paper presents initial results from energy models of 1699 office and retail buildings in San Francisco. Building stock data from public records were translated to create a BuildingSync file for each building which was stored in SEED. Each BuildingSync file was then translated to multiple OpenStudio Workflow files for EnergyPlus simulation to estimate energy savings of energy conservation measures (ECM). Energy savings predictions for each ECM were written back to an updated BuildingSync file for each building and re-uploaded to SEED. The distribution of baseline energy simulations was calibrated against the publicly disclosed distribution of energy benchmarking data to increase confidence in results.

Buildings in San Francisco consumed 52% of total primary energy. Improving building energy efficiency is one of the key strategies cities are adopting towards their energy and climate goals. Urban building energy models (UBEM) can support city managers to evaluate and prioritize energy conservation measures for investment and to design effective incentive and rebate programs. This paper introduces methods to develop a standardized dataset of city building stock, and it demonstrates the use of a UBEM tool, City Building Energy Saver (CityBES), for an urban-scale energy retrofit analysis of building stock in the city of San Francisco. CityBES is an open web-based data and computing platform providing city-scale building energy modeling and performance visualization and benchmarking. CityBES utilizes an international standard CityGML to represent the three-dimensional building stock in cities. As an application example, 940 office and retail buildings in six districts of northeast San Francisco were modeled and analyzed with CityBES to evaluate energy savings for five selected measures. The analysis found that replacing existing lighting with LED and adding an air economizer to HVAC systems are cost-effective measures with combined savings per building between 17% to 31%. The CityBES retrofit analysis feature does not require users to have deep knowledge of building systems or building energy models, which helps overcome barriers for city managers and their consultants to adopt UBEM.

The urban microclimate is essential for accurate simulation-based urban building energy modelling (UBEM). However, a high spatial-resolution microclimate can increase the computational resources demands of UBEM. Surrogate modelling is one of the promising approaches for fast UBEM. This study proposes a bidirectional Long Short-Term Memory (LSTM)-based approach for simulation-based UBEM surrogate modelling. The estimations are aggregated into census tracts using total building floor area. A case study using UBEM to estimate annual hourly building energy use and anthropogenic heat from all existing buildings in Los Angeles County found that most of the surrogate models can complete the annual hourly simulation within 90 minutes with a normalized mean absolute error lower than 10%, and that the bidirectional LSTM outperforms the standard LSTM in accuracy. This study demonstrates the advantages of bidirectional RNN architecture in building energy surrogate modelling and is expected to promote long-term and high-resolution UBEM with detailed microclimates.

We present unprecedented datasets of current and future projected weather files for building simulations in 15 major cities distributed across 10 climate zones worldwide. The datasets include ambient air temperature, relative humidity, atmospheric pressure, direct and diffuse solar irradiance, and wind speed at hourly resolution, which are essential climate elements needed to undertake building simulations. The datasets contain typical and extreme weather years in the EnergyPlus weather file (EPW) format and multiyear projections in comma-separated value (CSV) format for three periods: historical (2001-2020), future mid-term (2041-2060), and future long-term (2081-2100). The datasets were generated from projections of one regional climate model, which were bias-corrected using multiyear observational data for each city. The methodology used makes the datasets among the first to incorporate complex changes in the future climate for the frequency, duration, and magnitude of extreme temperatures. These datasets, created within the IEA EBC Annex 80 "Resilient Cooling for Buildings", are ready to be used for different types of building adaptation and resilience studies to climate change and heatwaves.

This study assessed the energy-saving and climate-adaptive potential of cool skin and ventilated cavity skin facade technologies in Seoul's high-rise apartment buildings. We created weather scenarios for historical, mid-term future, and long-term future conditions using Coordinated Regional Downscaling EXperiment (CORDEX) method. Building energy simulations were conducted on a South Korean high-rise apartment model to evaluate their performance under different weather conditions. The results indicate that cool skin and ventilated cavity skin technologies can save cooling energy during summers but lead to heating energy penalties in winters. Ventilated cavity skin outperforms cool skin, offering better cooling energy savings and reduced heating penalties. Combining both technologies yields the highest overall energy savings, with 7 %, 9 %, and 10 % cooling energy savings for cool skin, ventilated cavity skin, and the combined package, respectively. However, cool skin increases heating energy consumption by 5 %, while ventilated cavity skin has minimal impact on heating energy. These envelope technologies also reduce peak electricity demand by at least 5 %, 8 %, and 9 %, respectively. They contribute to heat stress reduction, enhance resilience, and decrease extreme heat risks for occupants during power outages by at least 18 % under various weather conditions. Considering the prevalence of aging high-rise apartments in South Korea, adopting these envelope renovation strategies can effectively reduce cooling loads, enhance thermal comfort, and boost resilience under future climates, while avoiding costly reconstruction.

The urban energy infrastructure is facing a rising number of challenges due to climate change and rapid urbanization. In particular, the link between urban morphology and energy systems has become increasingly crucial as cities continue to expand and become more densely populated. Achieving climate neutrality adds another layer of complexity, highlighting the need to address this relationship to develop effective strategies for sustainable urban energy infrastructure. The occurrence of extreme climate events can also trigger cascading failures in the system components, leading to long-lasting blackouts. This review paper thoroughly explores the challenges of incorporating urban morphology into energy system models through a comprehensive literature review and proposes a new framework to enhance the resilience of interconnected systems. The review emphasizes the need for integrated models to provide deeper insights into urban energy systems design and operation and addresses the cascading failures, interconnectivity, and compound impacts of climate change and urbanization on energy systems. It also explores emerging challenges and opportunities, including the requirement for high-quality data, utilization of big data, and integration of advanced technologies like artificial intelligence and machine learning in urban energy systems. The proposed framework integrates urban morphology classification, mesoscale and microscale climate data, and a design and operation process to consider the influence of urban morphology, climate variability, and extreme events. Given the prevalence of extreme climate events and the need for climate-resilient strategies, the study underscores the significance of improving energy system models to accommodate future climate variations while recognizing the interconnectivity within urban infrastructure.

The increasing frequency and severity of weather extremes caused by climate change evidence the need to assess buildings beyond their typical thermal and energy performance under normal operation. It is also essential to evaluate thermal resilience to safeguard occupants' health during extreme events and power outages. This study proposes a simulation framework to evaluate and enhance the thermal resilience of buildings against indoor overheating using an integrated set of performance metrics. This work also addresses how to aggregate resilience profiles of single buildings into the urban scale, supporting the evaluation of thermally resilient communities. This is the first step to connecting building and urban scales in a resilience analysis, seeking to further address other stakeholders' needs in the future. The application of the framework is exemplified through a case study considering three different climates in Brazil. This analysis allowed identifying cases with poor thermal resilience and essential dependence on air conditioning to guarantee the survivability of occupants during extreme hot weather. Nonetheless, by only changing the envelope's thermal transmittance and thermal mass, buildings' thermal autonomy increased up to 65% points and cooling loads were reduced by up to 61% in the hottest climate, São Luís. However, additional strategies are necessary to mitigate remaining indoor extreme thermal conditions, such as solar shading and increased air movement.

Radiative cooling uses high-emissivity materials to passively cool the surface of outdoor objects, such as building roofs on hot days. The issue of overcooling with this technology on cold days can be addressed by structures with thermal emissivity that is adaptive to temperature. Despite recent advances in temperature-adaptive structures, great challenges remain in their fabrication feasibility and unoptimized solar heating that may override the radiative cooling benefit. In this work, we develop a printable, emissivity-adaptive and albedo-optimized covering (PEAC) based on recyclable materials with roll-to-roll fabrication. A PEAC automatically switches its sky-window emissivity from 0.25 to 0.85 when the surface temperature exceeds a pre-set transition temperature, delivering an albedo optimized for maximal year-round energy saving or thermal comfort in a given climate.

With climate change leading to more frequent, more intense, and longer durations of extreme weather events such as heat waves and cold snaps, it is essential to maintain safe indoor environmental conditions for occupants during such events, which may coincide with, or even cause, power outages that expose residents to health risks. Analyzing the impacts of extreme weather events on the thermal resilience of buildings can help stakeholders (including occupants) understand the risk and inform them about mitigation and adaptation actions. Moreover, analyzing the technological, social and policy dimensions of thermal resilience is critical for climate-proofing buildings. This paper presents 10 questions that highlight the most important issues regarding the thermal resilience of buildings for occupants in the face of climate change. The proposed questions and answers aim to provide insights into current and future building thermal resilience research and applications, and more importantly to inspire new significant questions in the field.

Building energy flexibility is an important resource for a sustainable and resilient power grid, and an important measure to reduce utility costs for building owners. Quantifying energy flexibility for existing buildings can provide critical insights in optimizing their operation. Data-driven methods for building energy modeling and analytics are gaining popularity due to the increasingly available sensor and meter infrastructure, affordable computational resources, and advanced modeling algorithms. However, their application in quantifying the energy flexibility of real buildings is still limited due to the heterogeneous data types and limited data availability. This study proposes a framework for building-level data-driven energy flexibility quantification that considers different levels of data availability and use cases. Two case studies with real building data collected at different scales were conducted to demonstrate the proposed framework for different purposes.

Occupancy schedule is one of the key inputs in Building Energy Modeling (BEM) to reflect the interaction between buildings and occupants. Over the past decades, standardized occupancy schedules, developed mainly by engineering rule-of-thumb, have been widely used in BEM due to its simplicity and lack of real measured occupancy data. However, the BEM community has recognized their association with uncertainty and reliability in simulation results from BEM. This study introduces representative occupancy schedules in the U.S. residential buildings, derived from a large smart thermostat dataset and time-series K-means clustering, and an open-source tool to generate a stochastic residential occupancy schedule. Over 90,000 residential occupancy schedules were estimated from the ecobee Donate Your Data dataset. Then, the representative occupancy schedules were identified through clustering. This study further investigated the impacts of three parameters (day, house type, and state) on residential occupancy schedules. Then, a tool, the Residential Occupancy Schedule Simulator (ROSS), is developed using the representative occupancy schedules derived in this study. Details of this tool are presented in this paper. The derived representative occupancy schedules and the ROSS tool can help improve the energy modeling of residential buildings.

As buildings account for approximately 40% of global energy consumption and associated greenhouse gas emissions, their role in decarbonizing the power grid is crucial. The increased integration of variable energy sources, such as renewables, introduces uncertainties and unprecedented flexibilities, necessitating buildings to adapt their energy demand to enhance grid resiliency. Consequently, buildings must transition from passive energy consumers to active grid assets, providing demand flexibility and energy elasticity while maintaining occupant comfort and health. This fundamental shift demands advanced optimal control methods to manage escalating energy demand and avert power outages. Reinforcement learning (RL) emerges as a promising method to address these challenges. In this paper, we explore ten questions related to the application of RL in buildings, specifically targeting flexible energy management. We consider the growing availability of data, advancements in machine learning algorithms, open-source tools, and the practical deployment aspects associated with software and hardware requirements. Our objective is to deliver a comprehensive introduction to RL, present an overview of existing research and accomplishments, underscore the challenges and opportunities, and propose potential future research directions to expedite the adoption of RL for building energy management.

Energy flexibility, through short-term demand-side management (DSM) and energy storage technologies, is now seen as a major key to balancing the fluctuating supply in different energy grids with the energy demand of buildings. This is especially important when considering the intermittent nature of ever-growing renewable energy production, as well as the increasing dynamics of electricity demand in buildings. This paper provides a holistic review of (1) data-driven energy flexibility key performance indicators (KPIs) for buildings in the operational phase and (2) open datasets that can be used for testing energy flexibility KPIs. The review identifies a total of 48 data-driven energy flexibility KPIs from 87 recent and relevant publications. These KPIs were categorized and analyzed according to their type, complexity, scope, key stakeholders, data requirement, baseline requirement, resolution, and popularity. Moreover, 330 building datasets were collected and evaluated. Of those, 16 were deemed adequate to feature building performing demand response or building-to-grid (B2G) services. The DSM strategy, building scope, grid type, control strategy, needed data features, and usability of these selected 16 datasets were analyzed. This review reveals future opportunities to address limitations in the existing literature: (1) developing new data-driven methodologies to specifically evaluate different energy flexibility strategies and B2G services of existing buildings; (2) developing baseline-free KPIs that could be calculated from easily accessible building sensors and meter data; (3) devoting non-engineering efforts to promote building energy flexibility, standardizing data-driven energy flexibility quantification and verification processes; and (4) curating and analyzing datasets with proper description for energy flexibility assessm.

Rapid electrification of buildings at the district scale is needed for cities to achieve climate change mitigation goals. However, most electrification studies focus on either the single building level or the city/region building stock level, and depend on the slow and uncertain process of requesting personally identifiable customer energy usage data from utilities. To answer a key question facing local policymakers: “Where can electrification proceed at scale without first upgrading the grid?” this study aims to quantify and inform building electrification impacts at the district scale using detailed building energy modeling and based on public records datasets. We explore how energy efficiency retrofits can help mitigate increased peak electric demand, and quantify impacts to energy use and carbon emissions. Building energy models of a baseline, and scenarios of simple electrification, energy retrofits, and electrification in combination with retrofits were created and simulated for 54 commercial buildings in two contiguous districts of San Francisco. A simple electrification scenario increased annual electricity consumption but reduced annual site energy usage by 15% to 17%, mainly due to replacing inefficient gas furnaces and boilers with more efficient heat pumps. Peak demand increased 7.4% for Fisherman's Wharf (e.g. within the capacity of the existing power grid), while the Design District showed a marginal decrease. Annual carbon emissions were reduced by 46% and 37%. Combining electrification with efficiency upgrades reduced peak demand by 26% and 40%, and annual carbon emissions by 63% and 64% for the two districts. These results indicate that impacts of electrification depend on the mix of building uses within a district, and coupling electrification with energy efficiency upgrades is an effective strategy to decarbonize buildings while maintaining or reducing the peak electric demand.

Urban building energy model (UBEM) is a powerful tool to simulate performance and evaluate efficiency of upgrades for a group of buildings under the urban context. However, the larger the scale/number of buildings, more parameters must be collected to create energy models that cover each individual building, causing more uncertainties. To reveal this, this study created a UBEM for the mixed modern and historic buildings at a campus in China. The calibrated set of UBEMs with the modeled results meeting the 20% error range, were then used to evaluate uncertainties of energy-savings of four building energy retrofit (BER) measures. The first measure, BER 1, was to preserve the historic values of buildings; BER 2 to meet green building design standard; BER 3 to achieve 20% more savings than BER 2; and BER 4 to utilize renewable photovoltaic energy. For BER 1, BER 2, and BER 3, the energy savings of buildings of different ages varied within 10%–44%. For BER 4, the energy savings of buildings varied within 49%–505%, respectively, where the reason for higher than 100% is energy production is much higher than energy demand. Similar results can be concluded for building functions, for BER 1, BER 2, and BER 3, the energy-saving potentials varied within 6%– 45%, while 97%–492% to BER 4. This study can provide an important and significant reference to apply UBEM in evaluating energy-efficient retrofits as well as other energy-related studies that consider uncertainties.

Occupant behavior (OB) is one of the significant sources of uncertainty in building performance simulation. While OB modeling has received increased attention in the past decade, research on the degree of granularity or level of detail (LoD) required for representing occupants is still in the nascent stages. This paper analyzes the modeling and applicability of three LoDs to represent occupants in building performance assessment. A medium-sized prototype office building located in Chicago, Illinois is used as the simulation case study. Ten occupant-centric attributes are adopted to develop the LoDs for OB representation. We first demonstrate the different modeling approaches required for simulating the three fidelity levels. Later, we illustrate the suitability of the developed LoDs in supporting six building performance use cases across different lifecycle stages. This study intends to provide guidance for the building simulation community on appropriate OB representation to support various use cases.

Among the sufficiency, efficiency, and renewable frameworks for reducing energy use and energy-related carbon emissions, Building Energy Sufficiency (BES) is gaining attention from policy makers and engineers. Despite the significant role of the building sector in the success of national energy and climate plans, there is a lack of research on the drivers, technologies, and effective policy instruments required to achieve BES in the building operational phase. To fill this gap, this study presents a systematic review of the definition and paradigm of BES and concludes that BES should address both occupant demand and energy or emissions requirements simultaneously. The characteristics of occupant demand in building services are divided into four dimensions: time and space, quality and quantity, control and adjustment, and flexibility. Technical options regarding the building architecture, the envelope system, and the building energy system are reviewed. Finally, policy implications and recommendations are discussed. The multiple benefits and multidisciplinary nature of BES justify further research and accelerated policy implementation in developed and developing countries.

Long-term energy storage is expected to play a vital role in the deep decarbonization of building energy sectors, while enhancing the flexibility of buildings to withstand future climate variations. However, it is challenging to design distributed multi-energy systems (DMES) while taking into account the uncertainties introduced by climate change, since stochastic optimization of such systems is difficult. The present study introduces a stochastic optimization model to address this bottleneck, taking into account DMES including aquifer thermal energy storage (ATES) as the long-term thermal storage. For the first time, a novel optimization algorithm links ATES with the DMES optimization model with the support of a simplified geotechnical model. Subsequently, a case study was conducted, focusing on a residential district in Chicago where the impact of future climate condition, energy demand, and solar and wind energy potentials were evaluated using Weather Research and Forecasting (WRF) data (up to 2080) and the EnergyPlus model. The study revealed that ATES is an attractive way to improve the renewable energy penetration level and minimize the dependence on fossil fuels with reasonable support from the grid to assist the fluctuations in both demand and generation. Furthermore, ATES notably reduces fuel consumption and dependence while greatly enhancing the flexibility of the energy system to withstand fluctuations in demand and renewable energy generation brought by future climate variations. These qualities will make ATES an important part of distributed energy systems, even though it is not currently the lowest cost alternative due to lack of technology maturity. The design platform introduced in the present study can be used to design DMES enhancing flexibility to accommodate future climate variations.

Climate change leads to extreme climate events that result in frequent wildfires that cause numerous adverse societal impacts. Public Safety Power Shutoffs, adopted by utilities to minimize the risk of wildfires, pose many challenges to electricity consumers. Microgrids, have been proposed to improve the resilience of energy infrastructure during wildfire events for vulnerable communities. However, a comprehensive techno-economic and environmental assessment of the potential of such energy systems have not been performed. To address this research gap, the present study introduces a modeling framework, consisting of (1) clustering algorithms that identify the communities based on building footprint data, fire hazard severity, and renewable energy potential; (2) a building simulation model to quantify the energy demand; and (3) an energy system optimization model to assist the Microgrid design. A novel optimization tool was introduced to model Microgrids in wildland-urban interface, and subsequently, a comprehensive assessment was performed, focusing on seven localities from California, United States, with different climatic conditions. The study reveals that Microgrids can keep the average levelized energy cost and annual Public Safety Power Shutoffs below $0.3/kilowatt-hour (kWh) and 2%–3% (of the annual energy demand), respectively. Furthermore, renewable energy penetration levels can be maintained above 60% of the annual energy demand. Therefore, Microgrid may become an attractive solution to reduce the adverse impacts of wildfires and enhance the resilience of energy infrastructure. However, the study reveals that Microgrid cannot completely eliminate the Public Safety Power Shutoffs. The levelized cost and renewable energy generation curtailments (waste of renewable energy) become notably high when attempting to eliminate Public Safety Power Shutoffs completely. A notable reduction in energy storage cost is essential to achieve zero Public Safety Power Shutoffs, and this is expected with the evolution of energy storage technologies. The present study recommends Microgrids for communities affected by wildfires to enhance the resilience of energy infrastructure and protect the health and safety of residents. The modeling framework and optimization tool developed in this study can be used by stakeholders and their consultants to inform design and optimization of Microgrids for investment decision making.

Dense urban morphologies further amplify extreme climate events due to the urban heat island phenomenon, rendering cities more vulnerable to extreme climate events. Here we develop a modelling framework using multi-scale climate and energy system models to assess the compound impact of future climate variations and urban densification on renewable energy integration for 18 European cities. We observe a marked change in wind speed and temperature due to the aforementioned compound impact, resulting in a notable increase in both peak and annual energy demand. Therefore, an additional cost of 20‒60% will be needed during the energy transition (without technology innovation in building) to guarantee climate resilience. Failure to consider extreme climate events will lower power supply reliability by up to 30%. Energy infrastructure in dense urban areas of southern Europe is more vulnerable to the compound impact, necessitating flexibility improvements at the design phase when improving renewable penetration levels.

In recent years, advanced control strategies based on Deep Reinforcement Learning (DRL) proved to be effective in optimizing the management of integrated energy systems in buildings, reducing energy costs and improving indoor comfort conditions when compared to traditional reactive controllers. However, the scalability and implementation of DRL controllers are still limited since they require a considerable amount of time before converging to a near-optimal solution. This issue is currently addressed in literature through the offline pre-training of the DRL agent. However this solution results in two main critical issues: (1) the need to develop a building surrogate model to perform the training task, and (2) the need to perform a fine-tuning process over several training episodes to obtain a near-optimal control policy. In this context, this paper introduces an Online Transfer Learning (OTL) strategy that exploits two knowledge-sharing techniques, weight-initialization and imitation learning, to transfer a DRL control policy from a source office building to various target buildings in a simulation environment coupling EnergyPlus and Python. A DRL controller based on discrete Soft Actor–Critic (SAC) is trained on the source building to manage the operation of a cooling system consisting of a chiller and a thermal storage. Several target buildings are defined to benchmark the performance of the OTL strategy with that of a Rule-Based Controller (RBC) and two DRL-based control strategies, deployed in offline and online fashion. The strategy adopted for OTL emulates the real world implementation with a simulation process by implementing the transferred DRL agent for a single episode in the target buildings. Target buildings have the same geometrical features and are served by the same energy system as the source building, but differ in terms of weather conditions, electricity price schedules, occupancy patterns, and building envelope efficiency levels. The results show that the OTL strategy can reduce the cumulated sum of temperature violations on average by 50% and 80% respectively when compared to RBC and online DRL while enhancing the energy system operation with electricity cost savings ranging between 20% and 40%. The OTL agent performs slightly worse than the offline DRL controller but it does not require any modeling effort and can be implemented directly on target buildings emulating a real-world implementation.

We reviewed the present studies on the vulnerability and resilience of the energy ecosystem (most parts of the energy ecosystem), considering extreme climate events. This study revealed that the increased interactions formed during the transformation of the energy landscape into an ecosystem could notably increase the vulnerability of the energy infrastructure. Such complex ecosystem cannot be assessed using the present state of the art models used by the energy system modelers. Therefore, this study introduces a novel analogy known as the COVID analogy to understand the propagation of disruption within and beyond the energy ecosystem and organized the present state of the art based on the COVID analogy. The analogy helps to categorize the vulnerability of the energy infrastructure into three stages. The study revealed that although there are many publications covering the vulnerability and resilience of the energy infrastructure, considering extreme climate events, the majority are focused on the direct impact of extreme climate on the energy ecosystem. In addition, most of the studies do not consider the impact of future climate variations during this assessment. The propagation of disruptions was assessed mainly for wildfires and hurricanes. Further, there is a clear research gap in considering vulnerability assessment for interconnected energy infrastructure. The transformation of energy systems into a complex ecosystem notably increases the complexity, making it difficult to assess vulnerability and resilience. A shift from a centralized to decentralized modeling architecture could be beneficial when considering the complexities brought by that transformation. Hybrid models consisting of both physical and data-driven machine learning techniques could also be beneficial in this context.

Occupants are recipients of building services and have direct influence on building operation and performance. However, occupants are often addressed in simplified ways in building energy codes and standards that do not reflect the latest knowledge and scientific literature. This article describes an international review that we performed on 23 regions’ building energy codes and standards. We took two approaches: a quantitative approach focused on comparing occupant-related schedules, densities, and other values, and a qualitative approach whereby written requirements were analyzed. The results indicate major differences between code requirements or key modeling assumptions for code compliance (e.g., by a factor of two or more for plug load and occupant densities). Our review also revealed that few codes refer to occupants explicitly and requirements for building usability are rare. Based on the review and literature, we make some recommendations for how future codes could be adapted to better incorporate occupants.

Extreme hot and cold weather events are becoming more frequent, intense, and longer due to climate change. When these events occur coincidentally with power outages, the resulting extreme indoor temperatures pose a severe health hazard for occupants. This study conducted a holistic modeling and analysis of an assisted living facility, where senior residents live, to assess its thermal resilience performance under a six-day heat wave in 2015 and a three-day cold snap in 2021 with power outages. Impacts of 13 energy efficiency measures on thermal resilience and backup power capacity of the facility were evaluated. Three thermal resilience metrics: the SET (standard effective temperature) degree-hours, the Heat Index, and the Hours of Safety, were used and calculated from the EnergyPlus simulation models. Major findings are: (1) the facility would suffer from extreme temperatures during the cold and hot events without a power supply, not meeting the passive survivability requirements; (2) most passive envelope measures improve thermal resilience for both hot and cold events, but making the building envelope airtight results in conflicting performance between the hot and cold events; (3) natural ventilation is an effective measure to mitigate summer indoor overheating; and (4) the energy efficiency package can reduce backup power capacity by 19% for the three-day cold snap. It is recommended that building technologies and design strategies be evaluated to consider co-benefits of energy use, thermal resilience, and backup power needs through building energy codes or policies for existing and new buildings, which are transitioning for decarbonization and climate resilience.

California has a state-wide goal of carbon neutrality by 2045. Decarbonization for disadvantaged communities (DACs) poses extra challenges due to financial, informational, language, and other barriers. This paper presents the methodology, results, and analysis of energy efficiency measures (EEMs) to save energy, reduce CO2 emission, and promote clean energy access at the district scale for two DACs in Fresno, California. The methods are broadly applicable to other neighborhoods across the U.S. 22 EEMs were identified and modelled for all residential buildings in the two DACs both individually and as packages. Results show that for energy and CO2 reduction purposes, the top performing EEM package can decrease energy use and CO2 emissions by an average of 60%. For electrification, heat pump water heaters are a viable solution, coupled with air source or mini-split heat pumps. Replacing gasoline vehicles with electric vehicles is another important measure in electrification and reducing GHG emissions.

Energy flexibility of buildings can be an essential resource for a sustainable and reliable power grid with the growing variable renewable energy shares and the trend to electrify and decarbonize buildings. Traditional demand-side management technologies, advanced building controls, and emerging distributed energy resources (including electric vehicle, energy storage, and on-site power generation) enable the transition of the building stock to grid-interactive efficient buildings (GEBs) that operate efficiently to meet service needs and are responsive to grid pricing or carbon signals to achieve energy and carbon neutrality. Although energy flexibility has received growing attention from industry and the research community, there remains a lack of common ground for energy flexibility terminologies, characterization, and quantification methods. This paper presents a semantic ontology—EFOnt (Energy Flexibility Ontology)—that extends existing terminologies, ontologies, and schemas for building energy flexibility applications. EFOnt aims to serve as a standardized tool for knowledge co-development and streamlining energy flexibility related applications. We demonstrate potential use cases of EFOnt via two examples: (1) energy flexibility analytics with measured data from a residential smart thermostat dataset and a commercial building, and (2) modeling and simulation to evaluate energy flexibility of buildings. The compatibility of EFOnt with existing ontologies and the outlook of EFOnt's role in the building energy data tool ecosystem are discussed.

In recent years deep neural networks have been proposed as a lightweight data-driven model to capture high-dimensional, nonlinear physical processes to predict building thermal responses. However, the need of a large amount of data for the training process of deep neural networks clashes with the potential limited data availability in most existing or new buildings. Transfer learning aims to enhance the performance of a target learner exploiting knowledge from related and similar environments. This study conducted a suite of experiments that leveraged 250 data-driven models based on a synthetic dataset of a building archetype to study the influence of data availability, energy efficiency level, occupancy and climate for the transfer process of thermal dynamics. The performance of the transfer learning process was compared against a classical machine learning approach. The results suggest that building thermal dynamics can be effectively transferred under the same climatic conditions, increasing performance when dealing with different occupancy schedules, efficiency levels and low data availability. Furthermore, the paper compares the performance of both transfer learning and machine learning approaches in an online fashion, to support the implementation in real-world deployment.

Building demand flexibility (DF) research has recently gained attention. To unlock building DF as a predictable grid resource, we must establish a quantitative understanding of the resource size, performance variability, and predictability based on large empirical datasets. Researchers have proposed various sets of theoretical metrics to measure this performance. Some metrics have been applied to simulation results, but most fall short of exploring the complexities in real building applications. There are practical metrics used in individual demand response field studies but they alone cannot fulfil the job of DF benchmarking across a diverse group of buildings. The electrical grid's geographically diverse and changing nature presents challenges to comparing building DF performance measured under different conditions (i.e., benchmarking DF). To address this challenge, a novel DF benchmarking framework focused on load shedding and shifting is presented; the foundation is a set of simple, proven single-event metrics with attributes describing event conditions. These enable benchmarking and visualization in different dimensions for identifying trends that represent how these attributes influence DF. To test its feasibility and scalability, the DF framework was applied to two case studies of 11 office buildings and 121 big-box retail buildings with demand response participation data. These examples provided a pathway for using both building level benchmarking and aggregation to extract insights into building DF about magnitude, consistency, and influential factors. Potential applications of the framework and real-world values have been identified for grid and building stakeholders.

Rapid urbanization pressure and poverty have created a push for affordable housing within the global south. The design of affordable housing can have consequences on the thermal (dis)comfort and behaviour of the occupants, hence requiring an occupant-centric approach to ensure sustainability. This paper investigates occupant behaviour within the urban poor households of Mumbai, India and its impact on their thermal comfort and energy use. This study is a first-of-its-kind attempt to explore the socio-demographic characteristics and energy-related behaviour of low-income occupants within Indian context. Three occupant archetypes, Indifferent Consumers; Considerate Savers; and Conscious Conventionals, were identified from the behavioural and psychographic characteristics gathered through a transverse field survey. A two-step clustering approach was adopted for occupant segmentation that highlighted considerable diversity in occupants’ adaptation measures, energy knowledge, energy habits, and their pro-environmental behaviour within similar socio-economic group. Building energy simulation of the representative archetype behaviour estimated up to 37% variations for air-conditioned and up to 8% variation for fan-assisted naturally ventilated housing units during peak summer months. The results from this study establish the significance of occupant factors in shaping energy demand and thermal comfort within low-income housing and pave way for developing occupant-centric building design strategies to serve this marginalized population. The developed low-income occupant archetypes would be useful for architects and energy modelers to generate realistic energy use profiles and improve building performance simulation results.

Thermal energy storage (TES) for a cooling plant is a crucial resource for load flexibility. Traditionally, simple, heuristic control approaches, such as the storage priority control which charges TES during the nighttime and discharges during the daytime, have been widely used in practice, and shown reasonable performance in the past benefiting both the grid and the end-users such as buildings and district energy systems. However, the increasing penetration of renewables changes the situation, exposing the grid to a growing duck curve, which encourages the consumption of more energy in the daytime, and volatile renewable generation which requires dynamic planning. The growing pressure of diminishing greenhouse gas emissions also increases the complexity of cooling TES plant operations as different control strategies may apply to optimize operations for energy cost or carbon emissions. This paper presents a model predictive control (MPC), site demonstration and evaluation results of optimal operation of a chiller plant, TES and behind-meter photovoltaics for a campus-level district cooling system. The MPC was formulated as a mixed-integer linear program for better numerical and control properties. Compared with baseline rule-based controls, the MPC results show reductions of the excess PV power by around 25%, of the greenhouse gas emission by 10%, and of peak electricity demand by 10%.

Social houses built after the Second World War to accommodate workers and low-income families represent one of the major energy consumers and greenhouse gas emitters in the residential sector. Plans for their renovation are underway in all European countries, and the process is more complicated for Italian cities due to the lack of space and the large number of historical buildings. This study addresses this challenge by proposing a methodology to renovate a low-income district in the city of Venice using CityBES to model and evaluate energy conservation measures. CityBES is a web-based tool that allows users to employ urban building energy modeling for large-scale energy and retrofit analyses of building stocks. In the case study conducted for Venice's Santa Marta district, due to the particular context, four common energy conservation measures covering both the building envelope and heat generation boilers have been applied. The evaluation of energy-saving performances at the district level showed that the four measures together achieved 67% energy savings, an abatement in energy cost equal to 67%, and annual carbon dioxide emissions reduction of 1.1 MtCO2. The case study demonstrates a method and workflow replicable for energy retrofit analysis of building stocks in other historical districts.

Building performance simulation has been adopted to support decision making in the building life cycle. An essential issue is to ensure a building energy simulation model can capture the reality and complexity of buildings and their systems in both the static characteristics and dynamic operations. Building energy model calibration is a technique that takes various types of measured performance data (e.g., energy use) and tunes key model parameters to match the simulated results with the actual measurements. This study performed an application and evaluation of an automated pattern-based calibration method on commercial building models that were generated based on characteristics of real buildings. A public building dataset that includes high-level building attributes (e.g., building type, vintage, total floor area, number of stories, zip code) of 111 buildings in San Francisco, California, USA, was used to generate building models in EnergyPlus. Monthly level energy use calibrations were then conducted by comparing building model results against the actual buildings’ monthly electricity and natural gas consumption. The results showed 57 out of 111 buildings were successfully calibrated against actual buildings, while the remaining buildings showed opportunities for future calibration improvements. Enhancements to the pattern-based model calibration method are identified to expand its use for: (1) central heating, ventilation and air conditioning (HVAC) systems with chillers, (2) space heating and hot water heating with electricity sources, (3) mixed-use building types, and (4) partially occupied buildings.

Model Predictive Control (MPC) is a promising technique to address growing needs for heating, ventilation, and air-conditioning (HVAC) systems to operate more efficiently and with greater flexibility. However, due to a number of factors, including the required implementation expertise, lack of high quality data, and a risk-adverse industry, MPC has yet to gain widespread adoption. While many previous studies have shown the advantages of MPC, few analyzed the implementation effort and associated practical challenges. In addition, previous work has developed an open-source, Modelica-based tool-chain that automatically generates optimal control, parameter estimation, and state estimation problems aimed at facilitating MPC implementation. Therefore, this study demonstrates usage of this tool-chain to implement MPC in a real office building, discusses practical challenges of implementing MPC, and estimates the implementation effort associated with various tasks in order to inform the development of future workflows and serve as an initial benchmark for their impact on reducing implementation effort. This study finds that the implemented MPC saves approximately 40% of HVAC energy over the existing control during a two-month trial period and that tasks related to data collection and controller deployment activities can each require as much effort as model generation.

Energy-related behaviors of occupants constitute a key factor influencing building performance; accordingly, the measured occupant data can support the objective assessment of the indoor environment and energy performance of buildings, which can inform building design and operational decisions. Existing data schemas focus on metadata of sensors, meters, physical equipment, and IoT devices in buildings; however, they are limited in representing the metadata of occupant data, including occupants' presence in spaces, movement between spaces, interactions with building systems or IoT devices, and preference of indoor environmental needs. To address this gap, an extension to the widely adopted metadata schema, Brick, is proposed to represent the contextual, behavioral, and demographic information of occupants. The proposed extension includes four parts: (1) a new “Occupant” class to represent occupants' demography and energy related behavioral patterns, (2) new subclasses under the Equipment class to represent envelope system and personal thermal comfort devices, (3) new subclasses under the Point class to represent occupant sensing and status, and (4) new auxiliary properties for occupant interactable equipment to represent the level of controllability for each piece of equipment by occupants. The extension is implemented in the Brick schema and has been tested using multiple occupant datasets from the ASHRAE Global Occupant Database. The extension enables Brick schema to capture diverse types of occupant sensing data and their metadata for FAIR data research and applications.

Agent-based modeling is an advanced computational technique capable of representing complex and dynamic processes of human behavior in building performance simulation. Though the agent-based approach supports diverse applications concerning human behavior modeling within the built environment, there is no consensus on the optimal amount of information or level of granularity needed for occupant information representation. This paper attempts to formalize the level of details (LoD) needed for occupant behavior representation in agent-based environments. A novel framework, grounded on the concept of LoD, is proposed to select the required details in representing occupants in agent-based models. Ten attributes related to occupants' presence, movement, behavioral processes, and repertoire are considered to define the LoD. The framework identifies use case parameters as the guiding principle and allows a hybrid approach for selecting varying degrees of occupant attributes to serve the purpose of simulation. A discussion on the pertinence of different occupant behavior LoDs in relation to the desired objective and simulation context is also presented. The study intends to support the occupant behavior research by advancing agent-based occupant modeling in building performance simulation.