Results of the summarizing process for the arXiv paper: 2105.13399v1

The world is rapidly urbanizing, and the building industry accounts for over 40% of energy consumption in the United States. To address this issue, many cities are implementing ambitious energy-saving strategies by retrofitting existing buildings and constructing new sustainable communities. However, current urban building energy models (UBEM) have limitations in capturing inter-building interdependency due to their dynamic and non-linear characteristics. This can lead to inaccurate urban energy modeling as these models either ignore or oversimplify building interdependencies. To address this research gap, a team of researchers proposed a novel data-driven UBEM that synthesizes solar-based building interdependency and spatial-temporal graph convolutional network (ST-GCN) algorithm. The study used a university campus located in downtown Atlanta as an example to predict hourly energy consumption. The feasibility of the proposed model was tested by comparing its performance with other common time-series machine learning models. The results showed that the ST-GCN model outperformed all others, indicating its potential for accurately predicting urban building energy consumption. Additionally, the physical knowledge embedded in the model was well interpreted, highlighting how data-driven models integrated with engineering or physical knowledge can significantly improve urban building energy simulation. Overall, this study provides valuable insights into developing accurate UBEMs that can support designing energy-efficient communities for sustainable urban development. By addressing limitations in current models through innovative approaches like ST-GCN algorithms, we can create more effective solutions for reducing energy consumption in buildings and promoting sustainable living practices globally.

• The building industry accounts for over 40% of energy consumption in the United States
• Many cities are implementing ambitious energy-saving strategies by retrofitting existing buildings and constructing new sustainable communities
• Current urban building energy models have limitations in capturing inter-building interdependency due to their dynamic and non-linear characteristics
• A team of researchers proposed a novel data-driven UBEM that synthesizes solar-based building interdependency and spatial-temporal graph convolutional network (ST-GCN) algorithm
• The feasibility of the proposed model was tested by comparing its performance with other common time-series machine learning models, and the ST-GCN model outperformed all others
• The physical knowledge embedded in the model was well interpreted, highlighting how data-driven models integrated with engineering or physical knowledge can significantly improve urban building energy simulation.
• This study provides valuable insights into developing accurate UBEMs that can support designing energy-efficient communities for sustainable urban development.

Urban Building Energy Models: Exploring the Benefits of Data-Driven Solutions

As cities around the world continue to rapidly urbanize, energy consumption in buildings has become a major concern. The building industry accounts for over 40% of energy consumption in the United States alone, making it an important area to focus on when looking at ways to reduce overall energy use. To address this issue, many cities are implementing ambitious strategies such as retrofitting existing buildings and constructing new sustainable communities. However, current urban building energy models (UBEMs) have limitations in capturing inter-building interdependency due to their dynamic and non-linear characteristics. This can lead to inaccurate modeling results as these models either ignore or oversimplify building interdependencies.

In order to address this research gap, a team of researchers proposed a novel data-driven UBEM that synthesizes solar-based building interdependency and spatial-temporal graph convolutional network (ST-GCN) algorithm. The study used a university campus located in downtown Atlanta as an example to predict hourly energy consumption. The feasibility of the proposed model was tested by comparing its performance with other common time series machine learning models such as linear regression and random forest algorithms.

Results Show Improved Performance

The results showed that the ST-GCN model outperformed all others, indicating its potential for accurately predicting urban building energy consumption. Additionally, the physical knowledge embedded in the model was well interpreted, highlighting how data driven models integrated with engineering or physical knowledge can significantly improve urban building energy simulation accuracy compared with traditional methods like linear regression or random forest algorithms alone.

Conclusion: Promoting Sustainable Living Practices Globally

Overall, this study provides valuable insights into developing accurate UBEMs that can support designing energy efficient communities for sustainable urban development worldwide. By addressing limitations in current models through innovative approaches like ST-GCN algorithms we can create more effective solutions for reducing energy consumption in buildings and promoting sustainable living practices globally