Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow
Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow
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DOI：
10.1016/j.apenergy.2018.05.023
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发表时间：
2018-09
期刊：
影响因子：
11.2
通讯作者：
Alex Nutkiewicz;Zheng Yang;Rishee K. Jain
中科院分区：
文献类型：
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作者：
Alex Nutkiewicz;Zheng Yang;Rishee K. Jain
世界正在迅速城市化，能源密集型建筑环境对世界能源消耗和相关环境排放的影响越来越大。因此，人们付出了巨大的努力来开发能够准确建模和表征城市建筑能耗的方法。这些模型旨在利用基于物理的建筑能源模拟、降阶计算和统计学习方法来评估密集城市地区建筑物的能源性能。然而，当前的城市建筑能源模型在解释建筑物间能源动态和城市微气候因素方面的能力有限，而这些因素可能对建筑能源使用产生重大影响。为了克服这些限制，本文提出了一种新颖的数据驱动城市能源模拟（DUE-S）框架，该框架将基于网络的机器学习算法（ResNet）与工程模拟相结合，以更好地了解城市（单个建筑、街区、城市）中建筑物如何在多个时间（每小时、每天、每月）和空间尺度上消耗能源。我们在美国加利福尼亚州 22 座密集大学建筑的概念验证案例研究中验证了拟议的 DUE-S 框架。我们的结果表明，DUE-S 框架能够准确预测每小时、每天和每月的城市规模能源消耗。此外，我们的结果还表明，数据驱动和工程模拟方法的集成可以部分捕获建筑物间的能源动态和城市环境的影响，并值得未来的工作探索如何改进它们以预测郊区规模的能源预测（单个建筑、街区）。最后，成功预测和建模城市建筑的能源性能有可能为包括建筑师、工程师和政策制定者在内的各种城市可持续发展利益相关者的决策提供信息。
The world is rapidly urbanizing, and the energy intensive built environment is becoming increasingly responsible for the world’s energy consumption and associated environmental emissions. As a result, significant efforts have been put forth to develop methods that can accurately model and characterize building energy consumption in cities. These models aim to utilize physics-based building energy simulations, reduced-order calculations and statistical learning methods to assess the energy performance of buildings within a dense urban area. However, current urban building energy models are limited in their ability to account for the inter-building energy dynamics and urban microclimate factors that can have a substantial impact on building energy use. To overcome these limitations, this paper proposes a novel Data-driven Urban Energy Simulation (DUE-S) framework that integrates a network-based machine learning algorithm (ResNet) with engineering simulation to better understand how buildings consume energy on multiple temporal (hourly, daily, monthly) and spatial scales in a city (single building, block, urban). We validate the proposed DUE-S framework on a proof of concept case study of 22 densely located university buildings in California, USA. Our results indicate that the DUE-S framework is able to accurately predict urban scale energy consumption at hourly, daily and monthly intervals. Moreover, our results also demonstrate that the integration of data-driven and engineering simulation approaches can partially capture the inter-building energy dynamics and impacts of the urban context and merits future work to explore how they can be improved to predict sub-urban scale energy predictions (single building, block). In the end, successfully predicting and modeling the energy performance of urban buildings has the potential to inform the decision-making of a wide variety of urban sustainability stakeholders including architects, engineers and policymakers.