©2026 This excerpt taken from the article of the same name which appeared in ASHRAE Journal, vol. 68, No. 10, October 2026.
Practical Applications of AI in HVAC Design & Analysis
By Jacob Potoczak
Jacob Potoczak is founder and CEO of Arqen Labs in New York.
Artificial intelligence (AI) is increasingly used across commercial engineering disciplines, and HVAC design is no exception. This article examines the practical integration of AI tools into core HVAC engineering tasks. A structured case study demonstrates an AI-assisted workflow for a multiuse university laboratory building and addresses critical limitations, model transparency, assumption dependency and data governance, and outlines implementation considerations for engineering firms. Throughout, the emphasis remains on AI as an augmentation tool: the licensed mechanical engineer retains responsibility for all engineering judgments and final deliverables.
The complexity of modern HVAC design projects has grown substantially. Buildings now incorporate integrated energy systems, demand-controlled ventilation, variable refrigerant flow networks and detailed commissioning requirements. Concurrently, design schedules have compressed and deliverable quality expectations have risen. AI offers engineers a means to automate the most time-intensive, rule-based elements of the design workflow without surrendering professional responsibility for outcomes.
AI tools relevant to HVAC design span several technology categories. Computer vision and optical character recognition (OCR) enable automated parsing of floor plans and equipment schedules from PDF, CAD and building information modeling (BIM) sources. Machine learning regression models can predict peak cooling and heating loads from building geometry and construction parameters. Rule-based expert systems encode standards such as ASHRAE Standard 62.1 and ASHRAE/IES Standard 90.1 to check designs against code thresholds automatically. Large language models (LLMs) assist with specification drafting and report generation.
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