 Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflowAlex Nutkiewicz, Zheng Yang and Rishee K. Jain Applied Energy, 2018, vol. 225, issue C, 1176-1189 Abstract: 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. Keywords: Building energy; Data-driven; Machine learning; Multi-scale; Simulation; Urban energy modeling; Urban context (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:225:y:2018:i:c:p:1176-1189 Ordering information: This journal article can be ordered from DOI: 10.1016/j.apenergy.2018.05.023 Access Statistics for this article Applied Energy is currently edited by J. Yan More articles in Applied Energy from Elsevier |
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