MARL: Multi-scale Archetype Representation Learning for Urban Building Energy Modeling

TL;DR

This paper introduces MARL, a representation learning approach that extracts geometric features from building footprints to improve urban building energy modeling accuracy across different regions.

Contribution

MARL leverages VQ-AE to encode building geometries into latent vectors, enabling adaptive, multi-scale archetype generation and enhanced energy simulation accuracy.

Findings

MARL outperforms traditional archetypes in energy modeling accuracy.

Geometric embeddings improve energy consumption estimates.

Method is adaptable to various regional building footprints.

Abstract

Building archetypes, representative models of building stock, are crucial for precise energy simulations in Urban Building Energy Modeling. The current widely adopted building archetypes are developed on a nationwide scale, potentially neglecting the impact of local buildings' geometric specificities. We present Multi-scale Archetype Representation Learning (MARL), an approach that leverages representation learning to extract geometric features from a specific building stock. Built upon VQ-AE, MARL encodes building footprints and purifies geometric information into latent vectors constrained by multiple architectural downstream tasks. These tailored representations are proven valuable for further clustering and building energy modeling. The advantages of our algorithm are its adaptability with respect to the different building footprint sizes, the ability for automatic generation across multi-scale regions, and the preservation of geometric features across neighborhoods and local ecologies. In our study spanning five regions in LA County, we show MARL surpasses both conventional and VQ-AE extracted archetypes in performance. Results demonstrate that geometric feature embeddings significantly improve the accuracy and reliability of energy consumption estimates. Code, dataset and trained models are publicly available: https://github.com/ZixunHuang1997/MARL-BuildingEnergyEstimation