How do you see the role of emerging technologies, like AI and machine learning, in streamlining and enhancing building energy modeling workflows across the industry?
Dear Wondering,
Before the late 2000s, when machine learning (ML) and AI techniques began to gain traction, building energy modeling (BEM) was predominantly based on physics-driven methods, using tools like EnergyPlus to simulate complex thermodynamic and HVAC processes. As ML and AI technologies advanced, they expanded the scope of BEM to include advanced data-driven approaches.
Before the emergence of large language models (LLMs) in 2017, the integration of AI and ML into BEM primarily relied on smaller-scale techniques, including shallow ML methods like linear regression, support vector machines, and decision trees, as well as early deep learning algorithms such as Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). Data-driven BEM emerged as a valuable complement to physics-based tools like EnergyPlus, capturing building behavior and energy output based on historical and operational data rather than simulating physical processes. This approach offered a practical alternative when physics-based modeling was too resource-intensive or impractical. It proved particularly effective for tasks requiring speed or simplified representations, such as anomaly detection, short-term load forecasting, and occupant behavior modeling, especially in cases with abundant data but limited physics or building-specific details. Nonetheless, these methods remained data-intensive, required substantial expertise to develop and validate, and struggled with scalability across diverse building portfolios. Their applications were often limited to “regression” and “classification” tasks, reflecting the structured nature of BEM data and constraining their capacity to tackle more unstructured challenges in building energy modeling.
The arrival of large language models signaled a paradigm shift in how BEM is conducted, fundamentally streamlining and standardizing key processes while making advanced modeling capabilities more accessible. On the user-facing side, LLMs function as “front ends” that process unstructured or “fuzzy” inputs—ranging from architectural descriptions and construction notes to occupant behavior patterns—and convert them into structured, machine-readable formats. In many instances, they can even generate input files ready for physics-based simulators like EnergyPlus (e.g., IDF files). This capability substantially reduces the time and manual labor associated with data preparation, allowing modelers to focus on higher-level analytical tasks instead of repetitive data gathering and formatting. By minimizing the risk of user error, LLM-powered tools also elevate the overall reliability of BEM workflows.
Meanwhile, on the “back end,” LLMs serve as intelligent “brains” that replicate the decision-making processes typically handled by experienced practitioners. They can oversee tasks like preliminary model setup, calibration, and advanced analyses, striking a balance between standardized workflows and the unique conditions of individual projects. Moreover, LLMs can seamlessly integrate with structured data via code generation, automating iterative simulations, custom parametric studies, and comprehensive post-processing. These functionalities not only eliminate repetitive manual work but also open new possibilities for sophisticated modeling approaches.
LLM agents (https://www.anthropic.com/research/building-effective-agents) take this a step further, orchestrating entire end-to-end workflows and effectively managing complex iterative processes. Whether performing hierarchical, sequential, or reasoning-based (ReAct) tasks, these agents can coordinate large-scale simulations, refine models dynamically, and incorporate ongoing decision-making. By automating such multifaceted operations, LLM agents help democratize advanced BEM capabilities, allowing more professionals to harness high-level modeling even if they lack deep domain expertise.
One of the major advantages these technologies offer is their reduced dependency on extensive training data. Unlike traditional machine learning methods that often require large datasets, LLMs frequently perform well in zero-shot or few-shot contexts, lowering the barrier to entry for organizations with limited data resources. Instead of starting from scratch to build new models, stakeholders can tap into the powerful, pretrained capabilities of LLMs and tailor them through prompt engineering, focusing on effective usage rather than time-consuming development.
Looking ahead, this convergence of AI- and ML-driven approaches will radically streamline building energy modeling industry-wide. Novice users will be able to create BEM projects by conversationally interacting with an LLM—providing basic building information or uploading relevant files—and receiving accurate, actionable outputs. Seasoned practitioners will see their productivity skyrocket: LLMs can draft preliminary models, identify errors, offer debugging suggestions, and learn from past case studies. As a result, digital twins, continuous monitoring, and simulation-based optimizations are poised to become more prevalent, accelerating innovation in building design, operation, and sustainability.
In short, emerging AI and ML technologies are positioning the BEM field for unprecedented growth and efficiency. By simplifying data handling, automating complex tasks, and reducing the need for specialized expertise, they open the door for broader participation and higher-quality insights. This evolution not only saves time and money but ultimately advances our collective ability to design and operate buildings that meet ambitious energy and environmental goals.
Liang Zhang, Ph.D.
Assistant Professor, University of Arizona
