Evolutionary Multi-objective Optimization in Building Retrofit Planning Problem
Energy efficiency has been a primary subject of concern in the building sector, which consumes the largest portion of the world's total energy. Especially for existing buildings, retrofitting has been regarded as the most feasible and cost-effective method to improve energy efficiency. When pla...
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| Published in | Procedia engineering Vol. 145; pp. 565 - 570 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
2016
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1877-7058 1877-7058 |
| DOI | 10.1016/j.proeng.2016.04.045 |
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| Abstract | Energy efficiency has been a primary subject of concern in the building sector, which consumes the largest portion of the world's total energy. Especially for existing buildings, retrofitting has been regarded as the most feasible and cost-effective method to improve energy efficiency. When planning retrofit in public buildings, the most obvious objectives are to: (1) minimize energy consumption; (2) minimize CO2 emissions; (3) minimize retrofit costs; and (4) maximize thermal comfort; and one must consider these concerns together. The aim of this study is to apply evolutionary multi-objective optimization algorithm (NSGA-III) that can handle four objectives at a time to the application of building retrofit planning. A brief description of the algorithm is given, and the algorithm is examined using a building retrofit project, as a case study. The performance of the algorithm is evaluated using three measures: average distance to true Pareto-optimal front, hypervolume, and spacing. The results show that this study could be used to find a comprehensive set of trade-off scenarios for all possible retrofits, thereby providing references for building retrofit planners. These decision makers can then select the optimal retrofit strategy to satisfy stakeholders’ preferences. |
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| AbstractList | Energy efficiency has been a primary subject of concern in the building sector, which consumes the largest portion of the world's total energy. Especially for existing buildings, retrofitting has been regarded as the most feasible and cost-effective method to improve energy efficiency. When planning retrofit in public buildings, the most obvious objectives are to: (1) minimize energy consumption; (2) minimize CO2 emissions; (3) minimize retrofit costs; and (4) maximize thermal comfort; and one must consider these concerns together. The aim of this study is to apply evolutionary multi-objective optimization algorithm (NSGA-III) that can handle four objectives at a time to the application of building retrofit planning. A brief description of the algorithm is given, and the algorithm is examined using a building retrofit project, as a case study. The performance of the algorithm is evaluated using three measures: average distance to true Pareto-optimal front, hypervolume, and spacing. The results show that this study could be used to find a comprehensive set of trade-off scenarios for all possible retrofits, thereby providing references for building retrofit planners. These decision makers can then select the optimal retrofit strategy to satisfy stakeholders’ preferences. |
| Author | Son, Hyojoo Kim, Changwan |
| Author_xml | – sequence: 1 givenname: Hyojoo surname: Son fullname: Son, Hyojoo – sequence: 2 givenname: Changwan surname: Kim fullname: Kim, Changwan email: changwan@cau.ac.kr |
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| CitedBy_id | crossref_primary_10_1007_s42107_024_01143_4 crossref_primary_10_1016_j_jobe_2024_110422 crossref_primary_10_1007_s42107_024_01138_1 crossref_primary_10_1007_s42107_024_01203_9 crossref_primary_10_1016_j_energy_2019_01_164 crossref_primary_10_1007_s42107_024_01102_z crossref_primary_10_1016_j_enbuild_2024_114624 crossref_primary_10_1007_s42107_024_01157_y crossref_primary_10_1007_s42107_025_01305_y crossref_primary_10_1080_10286608_2019_1576646 crossref_primary_10_1007_s42107_025_01309_8 crossref_primary_10_1016_j_enbuild_2024_115216 crossref_primary_10_1016_j_heliyon_2025_e42480 crossref_primary_10_3390_su11051495 crossref_primary_10_1016_j_enbuild_2019_109690 crossref_primary_10_1016_j_jclepro_2022_133131 crossref_primary_10_1007_s42107_024_01014_y crossref_primary_10_1007_s42107_024_01170_1 |
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| Keywords | Building retrofit Energy consumption Thermal comfort CO2 emissions Retrofit costs Evolutionary multi-objective optimization |
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| SubjectTerms | Building retrofit CO2 emissions Energy consumption Evolutionary multi-objective optimization Retrofit costs Thermal comfort |
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