This presentation introduces a framework for early-phase building performance analysis that prioritizes speed, relevance, and clarity, with a particular emphasis on how analysis results are communicated and carried forward through the design process. In integrated project delivery environments, early design phases are often completed within only a few weeks. During this compressed timeframe, many fundamental architectural and building systems decisions are established and tend to persist into later phases, with limited opportunity for revision. As a result, building performance analysis must evolve into a fast, decision-oriented process capable of meaningfully informing design in near real time.

The presentation uses a renovation project as a case study to demonstrate how multiple consultants and engineers can collaboratively contribute to building performance analysis during a fast-moving early design phase. The case study illustrates an integrated workflow involving a mechanical engineer, façade engineer, electrical engineer, and building performance consultant, all contributing to a comprehensive building façade performance study. The analysis generated coordinated recommendations that directly informed mechanical system design, façade shading strategies, and building envelope material selection.

A key focus of the framework is the method used to present and communicate results. Study outputs are structured in a summarized yet detail-rich format that enables rapid understanding while preserving access to underlying assumptions and performance metrics. Results are delivered through layered visual outputs, beginning with high-level analysis summary tables and progressing to detailed performance metrics, annotated diagrams, and supporting narratives. This approach moves beyond simplified “good/better/best” comparisons by embedding quantitative thresholds, recommended design strategies, and key performance drivers directly into the visual communication.

This results communication method allows performance insights to be easily adopted and extended by different members of the design team, including mechanical engineers, façade consultants, and sustainability consultants, as the project advances into subsequent design phases. By translating analytical findings into clearly defined, actionable design guidance, the framework supports continuity and reduces the need for reanalysis or reinterpretation later in the process. An additional benefit of this approach is its accessibility to stakeholders with limited backgrounds in building performance analysis, such as architects and developers. The use of layered levels of detail allows users with different expertise to extract the information most relevant to their decision-making needs.

This clarity supports faster consensus, reduces uncertainty, and helps integrated project teams progress efficiently through early design, minimizing downstream revisions and associated cost and schedule impacts.

The growing availability of inherently DC technologies—including energy storage, electrical distribution (e.g., PoE), and end-use equipment (i.e., IT, lighting) — creates new and significant architectural and performance considerations for designers. System performance in hybrid AC-DC systems is more sensitive to design choices (e.g., AC vs. DC bus connection, power converter efficiency) as compared to conventional AC-only systems. Designers therefore may need modeling and simulation support to evaluate design alternatives and meet project goals related to peak demand management, reliability, and energy cost.

In practice, electrical design decisions are distributed across different phases of the building construction process and informed by a range of commercial and open-source tools focused on electrical safety. Electrical system performance is not significantly affected by design choices for AC-only systems, and as a result modeling and simulation to support power flow analysis is not commonplace. The open-source Building Electrical Efficiency Analysis Model (BEEAM) Modelica library can facilitate power-flow simulations of hybrid AC-DC building electrical systems modeled at the equipment-level; however, its use requires specialized Modelica expertise, and substantial time to construct whole-building models. Such expertise is uncommon among building system designers and electrical engineers, and manual model development does not scale well to the iterative, multi-phase nature of real projects.

This presentation introduces new modeling tools and a workflow that integrates with standard design practices and semi-automatically generates Modelica-based electrical system models to support design decision-making. The core innovations are Python middleware that a) create interoperable semantic models from Building Information Models (BIM), and b) extract relevant electrical system details from the semantic building model and programmatically assembles a corresponding Modelica simulation model using the BEEAM library. This workflow bridges BIM, semantic modeling, and power/energy modeling environments, and reduces the need for designers to develop deep Modelica or software engineering expertise.

The presentation briefly introduces the new tools and workflow, and discusses example design decisions that illustrate how the workflow can be used to evaluate system performance for alternative hybrid AC-DC electrical system configurations. Example simulation results will be presented for the design of a commercial office building that uses energy storage to maintain essential building electrical services during varying electrical outage durations. Finally, the presentation will address the potential applicability of this workflow to emerging wide-ranging data center design considerations. Data center applications – with their dense and predominantly DC ICT loads, extremely high-power demands, and high reliability requirements – may be the best use case for the presented capability, and even modest differences in system energy efficiency can have significant impacts on power demand and energy costs.

Prototype building energy models are widely used to represent typical building design and operational characteristics to support energy code development, impact assessments, policy analysis, and market adoption of new technologies on load demand, energy end use and emissions.

Since 1983, prototype development and adoption has grown exponentially with initiatives targeted to support specific policy and programmatic goals. At the national level, there are prototypes developed and maintained by national labs and at local level states and utilities have developed prototypes specific to address their needs. Building prototype models used in developing energy code requirements may not always be representative of the local construction practice, equipment performance among several other factors that often make the use of these of models for energy efficiency or load modeling. This problem is amplified with performance prediction of expected savings for large scale simulations at the city, state or national level.

This presentation will discuss the myths and realities and examine the historical rationale for developing standardized prototype models, emphasizing their role in enabling consistent, reproducible and scalable simulations across diverse building types, vintages and climate zones. One of the key takeaways from this presentation will be to provide best practices and guidelines for selection and use of prototypes by the energy modeling community.

Predictive control has significant potential to reduce operational costs in building HVAC systems while maintaining occupant comfort. However, widespread adoption remains limited due to the substantial effort required for system modeling and computationally intensive prediction. The increasing availability of data from building automation systems has enabled data-driven modeling approaches capable of fast predictions, but these methods often perform poorly when extrapolating to new operating conditions because they lack embedded physical knowledge. This research addresses this limitation through a gray-box modeling approach that combines reduced-order physics-based equations with parameters learned from data. The resulting models are fully differentiable and compatible with gradient-based optimization algorithms for predictive control. The proposed approach is demonstrated on a small multi-zone commercial building equipped with individual packaged HVAC units in each zone. The gray-box models are first evaluated across varying numbers of estimated parameters (e.g., representing the building envelope and inter-zone thermal couplings) to identify the minimum model complexity required to achieve adequate predictive accuracy. The approach is then compared against a black-box model under different training and testing data scenarios to assess relative data requirements across deployment conditions. The results provide building control designers with a practical methodology for developing fast, accurate, and control-oriented models suitable for predictive HVAC control.

Decarbonization of the heating systems in buildings can be a challenge especially for systems that rely on medium-temperature hot water (MTHW) loops when geothermal is not feasible. Standard air-source heat pumps (ASHPs) are insufficient, as they typically cannot supply water above ~140°, while many existing MTHW loops operate around 170°F supply and 150°F return. Geothermal with high-lift water-source heat pumps (WSHPs) is often the most cost-effective decarbonization strategy for MTHW, but space or building constraints can make this option impractical. Conventional alternatives, which require replacing hot water coils throughout the building to accommodate a lower supply temperature, are costly and invasive.

A promising alternative is a cascaded heat pump solution, in which ASHPs work in tandem with WSHPs. The WSHPs meet the loop’s temperature requirements by leveraging simultaneous heating and cooling loads, while ASHPs provide low-grade heat during winter. This configuration enables decarbonization of systems that would otherwise be difficult to electrify, with significantly less disruption and lower cost than converting to a lower-temperature loop.

Because the performance of the cascaded systems depends heavily on load coincidence, control sequencing, and source temperature availability, conventional whole-building energy modeling tools have limited ability to represent the interaction between the ASHPs and WSHPs beyond simplified approximations. To better evaluate this cascaded configuration, a component-based dynamic simulation was developed. In the simulation, the ASHPs and WSHPs are represented as interacting components with temperature-dependent performance curves, using control logic that stages equipment based on loop temperatures and building demand. This approach captures how water flow rates, temperatures, and heat transfer respond to changing loads and controls.

By simulating these interactions in detail, insight is provided into annual energy use and carbon performance, while also revealing how equipment selection and control strategies affect real-world operation. It demonstrates how detailed system simulation can guide decarbonization decisions, particularly for challenging cases such as cascaded heat pumps.

As of 2025, IESVE users have the ability to model Armstrong Templok ceiling tiles. This presentation will serve as a case study demonstrating proof of concept for utilizing the ceiling tiles in an elementary school in California that IES modeled as a part of its partnership with Armstrong. Presenters will walk through how to model the tiles in the VE, what kind of results modelers are able to explore, and what those modeled results mean for the energy consumption, cost, carbon, and LEED points for the project case study. The presentation will also explore potential environmental conservation measure bundles like widening the building’s setpoints and having a longer morning warm-up/cool-down period. Additionally, we’ll also identify contributing characteristics to the successful operation of the ceiling tiles like the presence of return air plenums, single- vs. multi- story buildings, and assessing a realistic amount of PCM in relation to other ceiling protrusions. Presentation attendees will have a better understanding of how to model phase change tiles in IESVE and why considering the tiles as an environmental conservation measure in their next project is worthwhile.

Too much of energy modeling practice is focused on answering questions that are not being asked. This lightning talk focuses on one MEP design firm's experience building a building performance department through identifying and addressing questions that actually drive design decisions.

All models are wrong, but some models are useful. As building simulation professionals, it is critical to understand where our services provide the most use, and tailor services to suit those uses. A model is not a good in and of itself.

This presentation will detail the essentials of thermal resilience analysis using building performance simulation tools, and is the summation of the work invested into a doctoral dissertation. Thermal resilience is a buildings ability to maintain or return to habitable conditions for occupants during extreme weather where mechanical space conditioning equipment fails or is rendered inoperable due to a loss of power.

Various aspects of resilience based simulation will be discussed. The evaluation of weather data and extrema will be covered, and resource recommendations provided to determine the extremity of the weather events that should be considered in the design process. The various thermal comfort metrics used for the evaluation of occupant impacts for resilience will be discussed, and recommendations for different types of occupants will be discussed. This presentation will discuss a resilience based modeling protocol and work flow, and the validation of said modeling protocol from measured data from the field.

Passive building techniques will also be covered at a high level to demonstrate design strategies that can be used to design a more resilient building.

The slides for this presentation will be prepared in a manner so that they can also be referenced by future modelers looking to explore thermal resilience in their design, including flow charts of the modeling process, resources and references for tools and datasets necessary to successfully analyze a building design for thermal resilience.