Cited by 223Building Energy and Comfort OptimizationIntegrated Energy Systems OptimizationUrban Heat Island Mitigation

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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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Nutkiewicz, Alex, Yang, Zheng, Jain, Rishee K.·2018·Applied Energy

DOI 10.1016/j.apenergy.2018.05.023

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Authors: Nutkiewicz, A., Yang, Z., & Jain, R. K.
Affiliations: Stanford University (US)
Corresponding author: Jain, Rishee K.
ORCID: Nutkiewicz, Alex
Jain, Rishee K.
Venue: Applied Energy
Publisher: Elsevier BV
Published: 2018-09
Volume: 225
Pages: 1176-1189
ISSN: 0306-2619 (ISSN-L)
DOI: 10.1016/j.apenergy.2018.05.023
OpenAlex: W2806066720
Cited by: 223
References: 76
Citations by year: 2026: 23 · 2025: 24 · 2024: 36 · 2023: 37 · 2022: 28 · 2021: 34 · 2020: 28 · 2019: 11 · 2018: 2
FWCI: 15.02
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (am, 127 d)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: Applied Energy (acceptedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Funders: UPS Foundation
Center for Integrated Facility Engineering
Terman Faculty Fellowship
US National Science Foundation (1461549, 1642315)
Topics: Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
Physical Sciences › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
Physical Sciences › Environmental Science › Environmental Engineering › Urban Heat Island Mitigation
Keywords: building energy simulation, machine learning, ResNet, urban microclimate, university buildings, urban sustainability

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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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