Data-driven Urban Energy Simulation (DUE-S): Integrating machine learning into an urban building energy simulation workflow
Data-driven Urban Energy Simulation (DUE-S): Integrating machine learning into an urban building energy simulation workflow
复制标题
数据驱动的城市能源模拟 (DUE-S)：将机器学习集成到城市建筑能源模拟工作流程中
DOI：
10.1016/j.egypro.2017.12.614
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Jain, Rishee K.
中科院分区：
文献类型：
--
作者：
Nutkiewicz, Alex;Yang, Zheng;Jain, Rishee K.
城市建筑能耗模型是一种新兴的工具，旨在分析和理解密集城市区域内多个建筑物的能源性能。然而，这些模型的准确性能预测仍然是一个挑战，因为它们无法解释城市地区的建筑物间能源动态和相互依赖性。本文分析了文献，以突出当前城市规模的能源模拟模型的局限性，并提出了一个新的数据驱动的城市能源模拟（DUE-S）的工作流程，能够捕捉建筑物的能源使用的建筑物间的影响。具体而言，DUE-S将数据驱动的机器学习模型与传统的基于物理的能源模拟相结合，以实现多个尺度（单个建筑，社区，城市）的更准确的模拟结果。对城市建筑进行更准确、更强大的能源性能表征和模拟，可以为早期建筑设计、建筑保护和投资组合管理以及城市能效政策制定提供宝贵的见解，这些都是帮助我们的城市向更可持续的能源未来过渡的关键。
Urban building energy models are emerging tools meant to analyze and understand the energy performance of multiple buildings within a dense urban area. However, accurate performance prediction of these models remains a challenge because of their inability to account for the inter-building energy dynamics and interdependencies in an urban area. This paper analyzes the literature to highlight the limitations of current urban scale energy simulation models and proposes a new Data-driven Urban Energy Simulation (DUE-S) workflow capable of capturing inter-building effects on a building’s energy usage. Specifically, DUE-S combines a data-driven machine learning model with a traditional physics-based energy simulation to enable more accurate simulation results on multiple scales (single building, community, urban). More accurate and robust energy performance characterization and simulations of urban buildings could provide valuable insight on early-stage building design, building conservation and portfolio management, and urban energy efficiency policy-making crucial to helping our cities transition to a more sustainable energy future.