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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 林偲妘 | zh_TW |
| dc.contributor.advisor | Szu-Yun Lin | en |
| dc.contributor.author | 黎仲達 | zh_TW |
| dc.contributor.author | Le Trong Dat | en |
| dc.date.accessioned | 2025-07-09T16:10:08Z | - |
| dc.date.available | 2025-07-10 | - |
| dc.date.copyright | 2025-07-09 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-07-01 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/97634 | - |
| dc.description.abstract | 過去研究指出，倘若被動式建築外部能源翻修僅以歷史氣象資料評估，將可能低估未來氣候變遷之影響。為解決此問題，本研究建構一套多目標評估架構，於 70 年生命週期內，同時採用基準之典型氣象年（Typical Meteorological Year）與依 CMIP6 情境調整之中期氣候預測氣象檔，評估各項整建方案。本研究以 Rhino, Grasshopper, Honeybee, Eppy, EnergyPlus等工具建構參數化且自動化之流程，模擬多種能源翻修策略；並以 NSGA-II 演算法求解投資成本、生命週期碳排放與熱不舒適時數之帕累托最適解。進一步結合 CRITIC 目標權重法與 TOPSIS 排序法，客觀提出優先翻修建議。結果顯示，於整棟建築全生命週期內，同時考量基準與未來氣候條件，可在僅需中度額外投資下，顯著降低碳排放並提升熱舒適度；其中最終排名第一之方案在兩種氣候情境下皆能維持穩定效益，證實納入未來氣候資訊與系統化最佳化之必要性。所提架構可於不同氣候情境下，提供具實證基礎之建築能源翻修決策參考。未來工作將進一步整合主動式 HVAC系統、建築再生能源與機率化成本分析，以更全面支援建築能源翻修之生命週期規劃。 | zh_TW |
| dc.description.abstract | While passive building envelope retrofits are often evaluated using historical weather data, such assessments may under represent future climate impacts. To address this, a multi-objective framework was developed to assess retrofit options over a 70-year lifecycle using both a baseline Typical Meteorological Year EnergyPlus Weather File (EPW) and a CMIP6-morphed EPW for projected mid-century conditions. A parametric Rhino/Grasshopper–Honeybee–Eppy workflow automated EnergyPlus simulations across various envelope configurations. Subsequently, NSGA-II identified Pareto-optimal trade-offs among investment cost, life-cycle carbon emissions, and thermal discomfort hours. An objective decision-analysis layer, employing CRITIC weighting and TOPSIS ranking, generated prioritized retrofit recommendations. When evaluated over the full service life under both baseline and future-climate inputs, the framework identified strategies that achieved notable emissions reductions and comfort improvements with moderate additional investment. In particular, the highest-ranked solution maintained consistent performance across both weather inputs, demonstrating the value of incorporating future-climate data and systematic optimization. Consequently, the framework provides clear, data-driven guidance for selecting resilient envelope upgrades under a low-forcing scenario. Future work will integrate active HVAC measures, on-site renewables, and probabilistic cost analysis to support comprehensive lifecycle retrofit planning. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-07-09T16:10:08Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-07-09T16:10:08Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Acknowledgements i
摘要 ii Abstract iii Table of Contents iv Table of Tables viii Table of Figures ix List of Abbreviations xi 1 Introduction 1 1.1 Background and Motivation 1 1.2 Research Objective 2 1.3 Overview of approach 2 2 Literature Review 3 2.1 Introduction to Building Retrofit 3 2.2 Challenges and Opportunities under Climate Change Impacts 4 2.3 Future Climate Data and Weather File Generation 6 2.3.1 Downscaling Methods and Weather File Generation Tools 7 2.3.2 Applications of Future Weather Files in Simulation Studies 7 2.4 Building Performance Simulation Tools 8 2.4.1 EnergyPlus as a Simulation Engine 8 2.4.2 Visual Interfaces for Early Design and Accessibility 9 2.4.3 Python-Based Tools for Advanced Automation and Optimization 10 2.5 Multi-Objective Optimization in Retrofit Planning 10 2.6 Multi-Criteria Decision Making (MCDM) Methods 12 2.7 Research gap 14 2.7.1 Application of CMIP6 in Downscaling & Future Climate Simulation 14 2.7.2 Integration of CMIP6 into MCDM 14 2.8 Summary 16 3 Methodology 17 3.1 Research process 17 3.2 Generating Future Weather 18 3.3 Creating IDF Models Using Rhino, Grasshopper, and Honeybee 19 3.4 Implementing Retrofit Strategies in IDF Files 20 3.5 Optimization Process (Simulation and Evaluation) 21 3.5.1 Simulation period 23 3.5.2 Objective Function Formulation 23 3.5.3 Optimization setting and data 27 3.6 Criteria Weighting Using the CRITIC Method 29 3.7 Solution Ranking Using the TOPSIS Method 32 3.8 Summary 34 4 Case Study 36 4.1 Description of the Case-Study Building 36 4.2 Simulation Settings and Assumption 36 4.2.1 Simulation Settings and Envelope Properties 36 4.2.2 Model Assumption 37 4.3 Retrofit Scenarios 37 5 Verification 39 5.1 Verification of Weather generation 39 5.1.1 Data and Methods 39 5.1.2 Verification result 40 5.1.3 Summary 42 5.2 Verification of Optimization Analysis 43 5.2.1 Evolution of the Solution Population 43 5.2.2 Crowding-Distance Illustration 45 5.2.3 Summary 46 5.3 TOPSIS and CRITIC 46 5.3.1 Verification procedure 47 5.3.2 TOPSIS verification result 48 5.3.3 CRITIC verification result 49 5.3.4 Summary 49 6 Result and discussion 50 6.1 Analysis of a representative climate scenario 50 6.1.1 Pareto front 50 6.1.2 Prominent solutions 53 6.1.3 CRITIC weighting 55 6.1.4 TOPSIS ranking 56 6.2 Comparison across future climate scenarios 58 6.2.1 Pareto fronts and comparison 58 6.2.2 Decision variables of all scenarios 60 6.2.3 CRITIC weighting for each scenario 63 6.2.4 TOPSIS ranking for whole scenarios 64 6.3 Discussion 67 7 Conclusion 68 Appendix 71 References 73 | - |
| dc.language.iso | en | - |
| dc.subject | 多目標最佳化 | zh_TW |
| dc.subject | 建築能源模擬 | zh_TW |
| dc.subject | 氣候變遷 | zh_TW |
| dc.subject | 建築能源翻修 | zh_TW |
| dc.subject | Multi-objective optimization | en |
| dc.subject | Building retrofit | en |
| dc.subject | Climate change | en |
| dc.subject | Energy simulation | en |
| dc.title | 未來氣候情境下建築能源翻修策略之多目標最佳化：兼顧經濟、環境與人體舒適 | zh_TW |
| dc.title | Multi-Objective Optimization of Energy Retrofit Strategies under Future Climate Scenarios: Balancing Economic, Environmental, and Human Comfort Objectives | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 詹瀅潔;許書廷 | zh_TW |
| dc.contributor.oralexamcommittee | Ying-Chieh Chan;Yu-Ting Hsu | en |
| dc.subject.keyword | 建築能源翻修,氣候變遷,建築能源模擬,多目標最佳化, | zh_TW |
| dc.subject.keyword | Building retrofit,Climate change,Energy simulation,Multi-objective optimization, | en |
| dc.relation.page | 80 | - |
| dc.identifier.doi | 10.6342/NTU202501347 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-07-02 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| dc.date.embargo-lift | 2028-06-26 | - |
| Appears in Collections: | 土木工程學系 | |
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| File | Size | Format | |
|---|---|---|---|
| ntu-113-2.pdf Until 2028-06-26 | 2.67 MB | Adobe PDF |
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