Buildings are active participants in increasingly complex energy systems. Building Energy Modeling (BEM) has a key role to play in planning and de-risking an equitable energy transition. BEM-backed “virtual buildings” are critical for understanding the interactive and complex nature of buildings in a diverse set of applications. Applications include efforts such as

* Hardware-in-the-Loop (HIL) to study equipment in a variety of deployment scenarios

* Control Hardware-in-the-Loop (CHIL) to assess commercial building control products

* Co-simulation to expose interactions of grid conditions and buildings for demand flexibility for utility scale application

* hands-on training tools for skilled trades workforce development.

Modeling requirements vary across these applications, but many software engineering tasks do not. The Alfalfa Virtual Building Service (AVBS, see https://github.com/NREL/alfalfa/wiki) is an open-source web service that abstracts the specifics of runtime interaction with OpenStudio, Modelica, and Spawn of EnergyPlus models behind a unified REST API.  Additionally, AVBS provides resources for cloud deployment and scaling to 100s of parallel BEMs. It also provides a growing library of modular Operational Technology (OT) integrations for emulation of real-world interfaces, i.e. BACnet, OpenADR. Finally, AVBS includes workflows to automate the population of communities of virtual buildings via URBANopt, ResStock and ComStock.

The Alfalfa Virtual Building Service is not the first tool to enable runtime interaction with building energy models. However, many of the prior solutions were either purpose built for very specific experiments or provided little to no support and documentation. AVBS seeks to provide a stable platform for experimentation with up-to-date documentation, continuing improvements, and a grounding in development best practices. In doing so it allows experimentalists to focus on the experiment, and not the limitations of the team’s ability to develop new capabilities within the scope of their project.

Needs for advanced and improved control strategies (CS) in building and district energy systems are growing due to requirements for reducing energy use, greenhouse gas emissions, and operating costs, providing flexibility to the electrical grid, as well as ensuring performance of novel hybrid and collective system architectures. Examples of such CS are advanced rule-based control, Model Predictive Control (MPC), and Reinforcement Learning. However, while these and other CS show promise, two challenges slow their widespread adoption: 1) The performance of each CS is typically demonstrated on individualized case studies and quantified using different performance indicators, making it difficult to properly benchmark and compare their performance, identify the most promising approaches, and identify needed further development. 2) Demonstrations in real buildings and district energy systems pose large operational risks and difficult environments for controlled experiments.

The building simulation community can address these challenges by providing suites of publicly available, high-fidelity simulation models, called test cases, to be used for benchmarking CS. Furthermore, providing a comprehensive framework to deploy, interact with, and generate key performance indicators (KPI) from these emulators would ensure their benchmarking capability and make them readily available to related control and data science fields outside of the building simulation community. Work is underway on the envisioned framework and test cases, called the Building Optimization Testing Framework (BOPTEST, see https://ibpsa.github.io/project1-boptest/), using state-of-the-art building simulation methods. This presentation will provide an overview of its development, availability, example use cases, and international development efforts underway in the recently started IBPSA Project 2.

The rise in developing novel HVAC control algorithms, driven by the demand for energy efficiency and operational adaptability, faces challenges in direct performance comparison due to specific case study contexts. Real-world field tests and controlled experiments in commercial buildings are often hindered by cost and logistical complexities. Standardized emulation frameworks, such as the Building Optimization Testing framework (BOPTEST), provide a valuable platform for validating, comparing, and troubleshooting the implementation of control algorithms. Large office buildings, constituting 50% of the floor area of total commercial building stocks in the U.S., present a particularly complex scenario with intricate building automation systems. Recognizing the need to overcome these challenges, we have developed a high-fidelity large office emulator for HVAC system control evaluation. This emulator is integrated into the standardized BOPTEST framework, offering a crucial test case for enhanced user accessibility.

Our approach involves the development of a Spawn-of-EnergyPlus-based large office building model under the BOPTEST framework. This model maintains the DOE prototype large office building geometry and load schedules while replacing the HVAC system with its Modelica counterpart. This substitution accurately captures the building's thermal load, HVAC system dynamics, and detailed control sequences. Importantly, it enables modeling of control logic explicitly at both the supervisory and local-loop levels, as well as at small timescales (i.e., seconds), overcoming limitations of EnergyPlus-only models in control performance evaluation. The large office emulator incorporates a central plant system with multiple chillers and boilers, along with multiple variable air volume (VAV) systems featuring terminal reheat. Comprehensive airside and plant-side controls, including mixing box/VAV terminal damper controls, fan/pump speed controls, air/water supply air temperature controls, plant equipment staging controls, and more, are incorporated. With 93 overwritable control setpoints and 315 measurement points for trending, users gain extensive insights from air handling units, zone VAV terminals, and plant-side systems.

As a demonstration of its capabilities, the large office emulator was used to assess the energy performance of ASHRAE Guideline 36 control sequences. Evaluating and implementing control strategies outlined in ASHRAE Guideline 36-2021 to replace conventional controls, our assessments under various load conditions revealed substantial energy savings—up to 20% in heating and 15% in cooling—compared to conventional controls.

In olden times, the FORTRAN namelist was the “easy” way for programs to read inputs, and building performance simulation programs were no different. Namelists were easy for the developer (up to a point), but less so for the user. Time passed, FORTRAN was left behind and it was not uncommon for new software to introduce new file formats specifically tailored to the needs of the software and its users. Unfortunately, as with namelists, it is hard to validate inputs when exactly one program understands the inputs. As these challenges became better understood, standard data formats (JSON, XML, and others) have emerged that simplify data exchange and offer users new ways to validate, operate on, and use data files. In the buildings area, examples include BuildingSync XML and EnergyPlus JSON. The talk will give a quick overview of namelists, the positional files that replaced them, and conclude with examples of the power of modern, standard file formats, particularly with respect to programmatic access and modification of models.

This talk presents an innovative prototype digital twin platform tailored for real-time building performance monitoring and simulation. Central to this platform is its ability to amalgamate various data sources, including operational data from buildings, weather conditions, and grid signals, thereby establishing a comprehensive base for advanced performance analytics. Utilizing the EnergyPlus model, the platform enables a dynamic simulation of diverse operational scenarios, providing an in-depth analysis and forecast of building performance under different conditions. This feature is crucial for stakeholders who aim to optimize building efficiency and sustainability. The platform is developed using the Python Django web framework, known for its robustness and scalability, which contributes to the efficient handling of complex data integrations and user interactions. The platform's unique capability to interface with physical assets within buildings, such as lighting, plug-loads, and HVAC systems, and extend to distributed energy resources like electric vehicles (EV), photovoltaic (PV) systems, and batteries, is facilitated through standard communication protocols, enhancing its practicality and scope of application. One of the key advantages of this platform is the ease and speed with which stakeholders can access both historical and real-time data on building performance. This accessibility is pivotal for facilitating informed decision-making, allowing for the assessment of various future operational scenarios. The platform is valuable to a broad spectrum of users, including researchers, building managers, and grid operators, by providing them with tools to analyze and improve building performance efficiently. In summary, this prototype digital twin platform not only offers a new solution for real-time operational building performance monitoring and analytics but also demonstrates remarkable flexibility and scalability, suggesting its potential for larger scale applications. It is exploration into the transformative potential of digital twin technology in building management for enhancing efficiency and sustainability in building operations.

Configuring building control, analytics, and applications hinges on effective data discovery. Presently, the absence of a standardized representation for building points necessitates an ad-hoc approach reliant on manufacturer-specific naming conventions. Semantic models emerge as a solution, organizing and contextualizing building data, thereby formalizing relationships across diverse sources of related building data in a machine-interpretable manner. Recent studies leverage automatic knowledge reasoning to amplify interoperability in applications spanning building control, automation, and analytics, facilitating context-aware monitoring and control of mechanical systems. This presentation reports on developing models to articulate building data and seamlessly integrating them into semantic models that enhance fault detection and diagnostics (FDD) and building control. In a case study involving a cutting-edge environmental chamber, data was incorporated into semantic models. The outcomes showcase how test data seamlessly mapped to equipment using ASHRAE 223P can be employed for high-performance HVAC system sequences of operation, following the guidelines set in ASHRAE Guideline 36 and utilizing the control description language outlined in ASHRAE SSPC 231P. Furthermore, this unified model eliminated the need for manual point list mapping in fault detection applications that was tested on the chamber.

The ability to control energy loads such as a building’s heating, ventilation, and air conditioning (HVAC) system can help facilitate increased penetration of variable renewable energy sources into the electric grid. To be able to control these HVAC systems more effectively, detailed simulations of the corresponding building physics is becoming increasingly important. These detailed simulations can be complex, nonlinear, and can require immense computational power when used in an advanced control method such as model predictive control (MPC), prompting the need to explore less computationally intensive strategies. In this work, a multi-fidelity approach is proposed to combine samples from a complex, high-fidelity model with a simple, low-fidelity model within the MPC control loop. More specifically, the parameters of a reduced-order, linear building model are periodically updated with knowledge from its high-fidelity counterpart – an EnergyPlus model – in an online fashion using a Gaussian Process surrogate model. Hence, highly accurate predictions of current and future conditions in a building are maintained with a substantially reduced computational burden compared to using the high-fidelity models alone. In other words, this linear parameter varying model preserves the low computational requirements of a low-order linear model while accurately modeling a building’s dynamics. This allows a building controller to take highly informed actions without requiring a large computational budget.