GreenPlanner: Practical Floorplan Layout Generation via an Energy-Aware and Function-Feasible Generative Framework

TL;DR

GreenPlanner is a comprehensive framework that automates the generation of energy-efficient, regulation-compliant floorplans, significantly reducing manual effort and improving design validity and efficiency.

Contribution

It introduces a unified, data-driven approach combining datasets, a fast evaluation model, and a generative framework for practical, constraint-aware floorplan design.

Findings

Accelerates evaluation by over 100,000 times with over 99% accuracy.

Eliminates invalid layout samples in the generation process.

Increases design efficiency by 87% compared to professional architects.

Abstract

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.