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
| Autor: | Alex Nutkiewicz, Rishee K. Jain, Zheng Yang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
020209 energy
Context (language use) 02 engineering and technology 010501 environmental sciences Management Monitoring Policy and Law Machine learning computer.software_genre Urban area 01 natural sciences 7. Clean energy 11. Sustainability 0202 electrical engineering electronic engineering information engineering Built environment 0105 earth and related environmental sciences geography geography.geographical_feature_category business.industry Mechanical Engineering Energy modeling Building and Construction Energy consumption General Energy Workflow 13. Climate action Proof of concept Artificial intelligence business computer Energy (signal processing) |
| Zdroj: | Applied Energy. 225:1176-1189 |
| ISSN: | 0306-2619 |
| DOI: | 10.1016/j.apenergy.2018.05.023 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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