A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Urban Building Energy Modeling: A Time-Series Building Energy Consumption Use Simulation Prediction Tool Based on Graph Neural Network
With the development of urbanization, the energy-intensive building environment in cities is becoming increasingly responsible for energy consumption and greenhouse emissions in the United States. As a result, great efforts have been put forth to develop tools and methodologies to forecast urban building energy consumption in a spatial and temporal dimension. However, existing physics-based and data-driven models are insufficient to consider the impacts of building dependencies and micro-climates efficiently, which can significantly affect model utility and accuracy. Due to configurations and characteristics of modern cities, the interdependencies among buildings, e.g., heat transfer between buildings and solar impacts, are most often non-linear, high-dimension, and highly dynamic, which increase the difficulties to model them. To address those challenges, a novel urban building energy model (UBEM) based on spatio-temporal graph convolutional network (STGCN) algorithm was proposed to predict temporal urban-level building energy consumption in cities and better understand the interactions between buildings. In particular, we took a campus in Atlanta, Georgia, as a case study to validate the accuracy of UBEM. Results indicate that the UBEM tool has significant improvement in simulation accuracy, and model explanation compared with physics-based models and pure data-driven models.
Urban Building Energy Modeling: A Time-Series Building Energy Consumption Use Simulation Prediction Tool Based on Graph Neural Network
With the development of urbanization, the energy-intensive building environment in cities is becoming increasingly responsible for energy consumption and greenhouse emissions in the United States. As a result, great efforts have been put forth to develop tools and methodologies to forecast urban building energy consumption in a spatial and temporal dimension. However, existing physics-based and data-driven models are insufficient to consider the impacts of building dependencies and micro-climates efficiently, which can significantly affect model utility and accuracy. Due to configurations and characteristics of modern cities, the interdependencies among buildings, e.g., heat transfer between buildings and solar impacts, are most often non-linear, high-dimension, and highly dynamic, which increase the difficulties to model them. To address those challenges, a novel urban building energy model (UBEM) based on spatio-temporal graph convolutional network (STGCN) algorithm was proposed to predict temporal urban-level building energy consumption in cities and better understand the interactions between buildings. In particular, we took a campus in Atlanta, Georgia, as a case study to validate the accuracy of UBEM. Results indicate that the UBEM tool has significant improvement in simulation accuracy, and model explanation compared with physics-based models and pure data-driven models.
Urban Building Energy Modeling: A Time-Series Building Energy Consumption Use Simulation Prediction Tool Based on Graph Neural Network
Cheng, Xiaoyuan (author) / Hu, Yuqing (author) / Huang, Jianxiang (author) / Wang, Suhang (author) / Zhao, Tianxiang (author) / Dai, Enyan (author)
ASCE International Conference on Computing in Civil Engineering 2021 ; 2022 ; Orlando, Florida
Computing in Civil Engineering 2021 ; 188-195
2022-05-24
Conference paper
Electronic Resource
English
| British Library Conference Proceedings | 2021
Building Energy Consumption Prediction Evaluation Model
| British Library Conference Proceedings | 2011
Prediction of building energy consumption by using artificial neural networks
| Tema Archive | 2009
Simulation on Building Energy Consumption for a Residential Building
| Trans Tech Publications | 2014