Encoding Urban Ecologies: Automated Building Archetype Generation through Self-Supervised Learning for Energy Modeling
- URL: http://arxiv.org/abs/2404.07435v1
- Date: Thu, 11 Apr 2024 02:29:08 GMT
- Title: Encoding Urban Ecologies: Automated Building Archetype Generation through Self-Supervised Learning for Energy Modeling
- Authors: Xinwei Zhuang, Zixun Huang, Wentao Zeng, Luisa Caldas,
- Abstract summary: Building sector has emerged as the predominant energy consumer and carbon emission contributor.
Existing building archetypes often fail to capture the unique attributes of local buildings and the nuanced distinctions between different cities.
This paper presents an alternative tool employing self-supervised learning to distill complex geometric data into representative, locale-specific archetypes.
- Score: 0.5399800035598186
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: As the global population and urbanization expand, the building sector has emerged as the predominant energy consumer and carbon emission contributor. The need for innovative Urban Building Energy Modeling grows, yet existing building archetypes often fail to capture the unique attributes of local buildings and the nuanced distinctions between different cities, jeopardizing the precision of energy modeling. This paper presents an alternative tool employing self-supervised learning to distill complex geometric data into representative, locale-specific archetypes. This study attempts to foster a new paradigm of interaction with built environments, incorporating local parameters to conduct bespoke energy simulations at the community level. The catered archetypes can augment the precision and applicability of energy consumption modeling at different scales across diverse building inventories. This tool provides a potential solution that encourages the exploration of emerging local ecologies. By integrating building envelope characteristics and cultural granularity into the building archetype generation process, we seek a future where architecture and urban design are intricately interwoven with the energy sector in shaping our built environments.
Related papers
- Global Transformer Architecture for Indoor Room Temperature Forecasting [49.32130498861987]
This work presents a global Transformer architecture for indoor temperature forecasting in multi-room buildings.
It aims at optimizing energy consumption and reducing greenhouse gas emissions associated with HVAC systems.
Notably, this study is the first to apply a Transformer architecture for indoor temperature forecasting in multi-room buildings.
arXiv Detail & Related papers (2023-10-31T14:09:32Z) - MARL: Multi-scale Archetype Representation Learning for Urban Building
Energy Modeling [0.5898893619901381]
We present Multi-scale Archetype Representation Learning (MARL), an approach that leverages representation learning to extract geometric features from a specific building stock.
MARL encodes building footprints and purifies geometric information into latent vectors constrained by multiple architectural downstream tasks.
Results demonstrate that geometric feature embeddings significantly improve the accuracy and reliability of energy consumption estimates.
arXiv Detail & Related papers (2023-09-29T22:56:19Z) - Building Coverage Estimation with Low-resolution Remote Sensing Imagery [65.95520230761544]
We propose a method for estimating building coverage using only publicly available low-resolution satellite imagery.
Our model achieves a coefficient of determination as high as 0.968 on predicting building coverage in regions of different levels of development around the world.
arXiv Detail & Related papers (2023-01-04T05:19:33Z) - Generative methods for Urban design and rapid solution space exploration [13.222198221605701]
This research introduces an implementation of a tensor-field-based generative urban modeling toolkit.
Our method encodes contextual constraints such as waterfront edges, terrain, view-axis, existing streets, landmarks, and non-geometric design inputs.
This allows users to generate many, diverse urban fabric configurations that resemble real-world cities with very few model inputs.
arXiv Detail & Related papers (2022-12-13T17:58:02Z) - Human-instructed Deep Hierarchical Generative Learning for Automated
Urban Planning [57.91323079939641]
We develop a novel human-instructed deep hierarchical generative model to generate optimal urban plans.
The first stage is to label the grids of a target area with latent functionalities to discover functional zones.
The second stage is to perceive the planning requirements to form urban functionality projections.
The third stage is to leverage multi-attentions to model the zone-zone peer dependencies of the functionality projections to generate grid-level land-use configurations.
arXiv Detail & Related papers (2022-12-01T23:06:41Z) - Latent Diffusion Energy-Based Model for Interpretable Text Modeling [104.85356157724372]
We introduce a novel symbiosis between the diffusion models and latent space EBMs in a variational learning framework.
We develop a geometric clustering-based regularization jointly with the information bottleneck to further improve the quality of the learned latent space.
arXiv Detail & Related papers (2022-06-13T03:41:31Z) - eBIM-GNN : Fast and Scalable energy analysis through BIMs and Graph
Neural Networks [0.0]
Building Information Modeling has been used to analyze as well as increase the energy efficiency of the buildings.
Current cities which were built without the knowledge of energy savings are now demanding better ways to become smart in energy utilization.
We propose a method to creation of prototype buildings that enable us to match and generate statistics very efficiently.
arXiv Detail & Related papers (2022-05-21T03:24:03Z) - Zero Shot Learning for Predicting Energy Usage of Buildings in
Sustainable Design [2.929237637363991]
The 2030 Challenge is aimed at making all new buildings and major renovations carbon neutral by 2030.
It is important to understand how the various building factors contribute to energy usage of a building, right at design time.
Rich training datasets are needed for AI-based solutions to achieve good prediction accuracy.
arXiv Detail & Related papers (2022-02-10T18:08:58Z) - Times Series Forecasting for Urban Building Energy Consumption Based on
Graph Convolutional Network [20.358180125750046]
Building industry accounts for more than 40% of energy consumption in the United States.
UBEM is the foundation to support the design of energy-efficient communities.
Data-driven models integrated engineering or physical knowledge can significantly improve the urban building energy simulation.
arXiv Detail & Related papers (2021-05-27T19:02:04Z) - Methodological Foundation of a Numerical Taxonomy of Urban Form [62.997667081978825]
We present a method for numerical taxonomy of urban form derived from biological systematics.
We derive homogeneous urban tissue types and, by determining overall morphological similarity between them, generate a hierarchical classification of urban form.
After framing and presenting the method, we test it on two cities - Prague and Amsterdam.
arXiv Detail & Related papers (2021-04-30T12:47:52Z) - Towards a Peer-to-Peer Energy Market: an Overview [68.8204255655161]
This work focuses on the electric power market, comparing the status quo with the recent trend towards the increase in distributed self-generation capabilities by prosumers.
We introduce a potential multi-layered architecture for a Peer-to-Peer (P2P) energy market, discussing the fundamental aspects of local production and local consumption as part of a microgrid.
To give a full picture to the reader, we also scrutinise relevant elements of energy trading, such as Smart Contract and grid stability.
arXiv Detail & Related papers (2020-03-02T20:32:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.