arXiv cs.AI· Judah Goldfeder, Ayush Rai, Philippe M. Wyder, Na Li, Victoria Dean, Donna Vakalis, Jiong Lin, Bing Dong, Michael Rossetti, Nathan Kutz, Sohei Arisaka, John Sipple, Hod Lipson, Yoshua Bengio·· 3 天前AI 评分23
智能建筑挑战赛：学习可规模化真实世界优化的基础模型
The Smart Buildings Challenge: Learning Foundation Models for Real-World Optimization at Scale
AI 导读
研究者提出"智能建筑挑战赛"，面向 NeurIPS 2026，征集能跨建筑泛化的建筑基础模型。参赛者需在模拟建筑集上构建控制策略，并在未见过的建筑上评估，这些建筑在规模、布局、天气、材料属性和 HVAC 系统上各不相同。建筑占美国碳排放近 40%，其中 HVAC 占建筑排放约 40-60%，而现有控制策略普遍缺乏策略迁移能力。
正文 · 原文
Authors:Judah Goldfeder, Ayush Rai, Philippe M. Wyder, Na Li, Victoria Dean, Donna Vakalis, Jiong Lin, Bing Dong, Michael Rossetti, Nathan Kutz, Sohei Arisaka, John Sipple, Hod Lipson, Yoshua Bengio
Abstract:Buildings cause nearly 40\% of carbon emissions in the US. Of total building emissions, HVAC systems are responsible for approximately 40-60\% and account for 15\% of global energy consumption. Despite advances in artificial intelligence, most buildings still operate as inefficiently as they did decades ago. Building optimization, perhaps more so than any other optimization problem, has enormous potential for Machine Learning (ML) driven impact on climate change, in both the short and long term. One of the biggest obstacles for building optimization is the lack of policy transfer. Most building control systems, whether simulation or learning based, don't generalize: they only work well for the building they were developed for, but not for other buildings. To solve this problem, we propose a challenge for the development of building foundation models that easily adapt to new buildings with minimal configuration and thereby enable building optimization to scale across the globe. Participants will build control policies on a set of simulated buildings, and have their policies evaluated on an unseen set of buildings. The buildings vary in size, layout, weather, material properties, and HVAC system. Successful outcomes from this initiative promise a massive leap forward in global sustainability efforts and demonstrate the great potential of ML to combat climate change.
| Comments: | This is for a Neurips 2026 competition |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.18511 [cs.AI] |
| (or arXiv:2610.18511v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.18511 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Judah Goldfeder [view email]
[v1]
Tue, 13 Oct 2026 15:06:08 UTC (1,991 KB)
来源：arXiv cs.AI · arxiv.org