Issue |
E3S Web of Conf.
Volume 562, 2024
BuildSim Nordic 2024
|
|
---|---|---|
Article Number | 05007 | |
Number of page(s) | 13 | |
Section | Near-Zero Buildings (ZEB) & Renewable Energy | |
DOI | https://doi.org/10.1051/e3sconf/202456205007 | |
Published online | 07 August 2024 |
Data-Driven Research on Energy-Efficient Retrofit and Multi-Objective Optimization of Urban Building Clusters
School of Architecture, Southeast University, Sipailou #2, Nanjing, China
* Corresponding author: weiwang@seu.edu.cn
Urban energy-efficient retrofit is an important path to reduce energy consumption and carbon emission. However, balancing environmental and economic benefits and making choices among numerous retrofit packages is challenging for decision-makers. This study proposes a data-driven framework that integrates physical UBEM and machine learning model to evaluate the energy retrofit performance of urban building clusters and assists decision-makers in rapidly selecting the optimal energy-efficient retrofit packages through NSGA-II. The feasibility of the proposed framework is validated using the building clusters of Sipailou campus in Southeast University as a case study and identified 25, 19, and 13 optimal retrofit packages for office, public and education building clusters that minimizes the total energy consumption and carbon emissions while maximizing economic benefits from 343 retrofit packages of each cluster in a 30-year retrofit period.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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