Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.00406 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918224269934592 |
|---|---|
| author | Zeng, Pengyu Dai, Yuqin Yin, Jun Zhong, Jing Han, Ziyang Shi, Chaoyang Jin, ZhanXiang Jiang, Maowei Han, Yuxing Lu, Shuai |
| author_facet | Zeng, Pengyu Dai, Yuqin Yin, Jun Zhong, Jing Han, Ziyang Shi, Chaoyang Jin, ZhanXiang Jiang, Maowei Han, Yuxing Lu, Shuai |
| contents | Building design directly affects human well-being and carbon emissions, yet generating spatial-functional and energy-compliant floorplans remains manual, costly, and non-scalable. Existing methods produce visually plausible layouts but frequently violate key constraints, yielding invalid results due to the absence of automated evaluation. We present GreenPlanner, an energy- and functionality-aware generative framework that unifies design evaluation and generation. It consists of a labeled Design Feasibility Dataset for learning constraint priors; a fast Practical Design Evaluator (PDE) for predicting energy performance and spatial-functional validity; a Green Plan Dataset (GreenPD) derived from PDE-guided filtering to pair user requirements with regulation-compliant layouts; and a GreenFlow generator trained on GreenPD with PDE feedback for controllable, regulation-aware generation. Experiments show that GreenPlanner accelerates evaluation by over $10^{5}\times$ with $>$99% accuracy, eliminates invalid samples, and boosts design efficiency by 87% over professional architects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_00406 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GreenPlanner: Practical Floorplan Layout Generation via an Energy-Aware and Function-Feasible Generative Framework Zeng, Pengyu Dai, Yuqin Yin, Jun Zhong, Jing Han, Ziyang Shi, Chaoyang Jin, ZhanXiang Jiang, Maowei Han, Yuxing Lu, Shuai Artificial Intelligence Machine Learning Building design directly affects human well-being and carbon emissions, yet generating spatial-functional and energy-compliant floorplans remains manual, costly, and non-scalable. Existing methods produce visually plausible layouts but frequently violate key constraints, yielding invalid results due to the absence of automated evaluation. We present GreenPlanner, an energy- and functionality-aware generative framework that unifies design evaluation and generation. It consists of a labeled Design Feasibility Dataset for learning constraint priors; a fast Practical Design Evaluator (PDE) for predicting energy performance and spatial-functional validity; a Green Plan Dataset (GreenPD) derived from PDE-guided filtering to pair user requirements with regulation-compliant layouts; and a GreenFlow generator trained on GreenPD with PDE feedback for controllable, regulation-aware generation. Experiments show that GreenPlanner accelerates evaluation by over $10^{5}\times$ with $>$99% accuracy, eliminates invalid samples, and boosts design efficiency by 87% over professional architects. |
| title | GreenPlanner: Practical Floorplan Layout Generation via an Energy-Aware and Function-Feasible Generative Framework |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2512.00406 |