Affiliation:
1. School of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China
Abstract
The existing building stock faces the challenge of low energy efficiency and requires renovation and upgrading to meet society′s goals of carbon reduction and sustainable development. This study presents an optimization framework utilizing genetic algorithms to develop robust retrofit plans that balance the need for improved energy efficiency, cost-effectiveness considerations for householders, and uncertainties regarding climate conditions. A case study of an aged residential building in a hot and humid region of China is used to demonstrate the proposed method. The optimization results show a potential energy demand reduction of 81.5%. However, due to the relatively long time required to realize economic benefits from high investments, short-term optimization tends to favor solutions with high energy demand and low primary costs. To effectively reduce carbon emissions, it is recommended to consider the long-term economic benefits of retrofits and prioritize solutions with high energy efficiency. However, it is important to acknowledge that the expensive nature of retrofit investments may pose barriers to residents. Society should provide adequate support and guidance to facilitate residential renovation efforts.
Funder
Guangdong Basic and Applied Basic Research Foundation
State Key Laboratory of Subtropical Building Science
Guangzhou Municipal Science and Technology Bureau
Science, National Undergraduates′ Innovation and Entrepreneurship Training Program
Technology Program of Guangzhou University
Subject
Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction
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