Skip to content
← All posts

We built an AI factory for HVAC control

GPT-Lab · gpt-lab.eu · By Rajratan Wankhade · August 28, 2026

What does it take to trust AI-driven HVAC optimization? Our AI Model Factory combines agents, machine learning, reinforcement learning and deterministic checks in a governed workflow designed for messy, real-world building data. The post We built an AI factory for HVAC control appeared first on GPT-Lab.

Read on GPT-Lab → Opens the original article in a new tab.

More from the blog

Related papers

#machine learning Open access May 2025

Token-Mol 1.0: tokenized drug design with large language models

Token-Mol is presented, a token-only 3D drug design model that encodes both 2D and 3D structural information, along with molecular properties, into discrete tokens, which introduces a Gaussian cross-entropy loss function tailored for regression tasks, enabling superior performance across multiple downstream applications.

Ji-Ke Wang, Rui Qin, Mingyang Wang et al. · 30 citations · ⚡1
#machine learning Open access Nov 2025

mRNABERT: advancing mRNA sequence design with a universal language model and comprehensive dataset

The authors develop mRNABERT, a foundational AI model that designs entire mRNA sequences and demonstrates superior performance across comprehensive benchmarks, which signifies a substantial leap forward in mRNA research and therapeutic development.

Ying Xiong, Aowen Wang, Yu Kang et al. · 23 citations · ⚡1

Time for AI (Ethics) Maturity Model Is Now

It is argued that AI software is still software and needs to be approached from the software development perspective, and whether the focus should be on AI ethics or the quality of an AI system, called a maturity model for the development of AI systems is discussed.

Ville Vakkuri, Marianna Jantunen, Erika Halme et al. · 17 citations · ⚡1
#machine learning Open access Sep 2025

Unified and explainable molecular representation learning for imperfectly annotated data from the hypergraph view

OmniMol is presented, a framework using hypergraphs to improve predictions of molecular properties, addressing challenges of imperfect data annotation and enhancing model explainability, and achieves state-of-the-art performance in properties prediction.

Bowen Wang, Junyou Li, Donghao Zhou et al. · 11 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.