Implementing AI in Smart Buildings: A Modular, Proof-of-Concept Approach
Bibliographic record
Abstract
The integration of Artificial Intelligence (AI) into Building Automation Systems (BAS) has potential to enhance operational efficiency, adaptability, and predictive capabilities. Traditional BAS rely on static rule-based automation, limiting their responsiveness to dynamic conditions. This paper proposes a modular AI-driven framework that utilizes machine learning techniques on a commercial BAS dataset to optimize energy usage, improve occupant comfort, and could be easily adapted for demand response applications. Our framework, designed for seamless integration with existing infrastructure, enhances traditional BMS through generative AI agents employing tool calling and Retrieval-Augmented Generation (RAG), offering robust insights and advanced data exploration capabilities for facility managers. By combining the proven reliability of traditional BAS with the advanced capabilities of modern AI techniques, our modular system significantly advances building performance and sustainability. We evaluate cloud, Semi-Local, and fully local AI deployments, highlighting trade-offs in accuracy, latency, and privacy. Results show that while cloud models offer high accuracy, Semi-Local architectures provide a balance between performance and security. Advances in lightweight LLMs and edge computing indicate growing feasibility for fully local AI solutions. This framework demonstrates the potential of AI-enhanced BAS, offering a scalable approach to smart building management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".