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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳靜枝 | zh_TW |
| dc.contributor.advisor | Ching-Chin Chern | en |
| dc.contributor.author | 鍾靖詮 | zh_TW |
| dc.contributor.author | Ching-Chuan Chung | en |
| dc.date.accessioned | 2025-08-21T16:18:05Z | - |
| dc.date.available | 2025-08-22 | - |
| dc.date.copyright | 2025-08-21 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-08-04 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99078 | - |
| dc.description.abstract | 面對全球減碳壓力與淨零排放目標，碳足跡管理及預測議題漸趨重要；雖然製造業等領域在過去存在相關研究，但對於如餐廳、自助洗衣店等容量限制服務業，相關研究仍相對稀少，使產業難以量化自身碳足跡排放量，也使政策制定缺乏實證依據。
本研究以自助洗衣業為範例，提出一個碳足跡計算及管理與預測的框架。透過業者提供的IoT 資料，整合機台的電力、水與廢水三項主要耗用量，並提出以排放係數為基礎的碳足跡計算方式；其次，再以歷史營運紀錄訓練時間序列模型，預測未來十二個月碳足跡的趨勢；考量新店面或新機台常面臨資料不足，本研究也設計分群模型，依設備使用型態與環境條件尋找相似群組，而分開處理不同量級的資源，則可以進一步提供模型穩定度及精確度。 研究結果顯示，所建構的碳足跡時間序列模型與分群模型均具良好準確度，該框架不僅可協助自助洗衣業據此制定減排策略，也能進一步延伸至餐廳、電動車充電椿等其他容量限制服務領域，做為永續營運之參考。 | zh_TW |
| dc.description.abstract | Facing growing global pressure to reduce carbon emissions and achieve net-zero targets, carbon-footprint management and forecasting have become increasingly important. Although previous studies exist in sectors such as manufacturing, research on capacitated service industries—such as restaurants and self-service laundromats—remains relatively scarce, making it difficult for these industries to quantify their own carbon-footprint emissions and leaving policymakers without empirical evidence.
Using the self-service laundry industry as an example, this study proposes a framework for carbon-footprint calculation, management, and forecasting. IoT data supplied by operators are used to integrate the three main resource consumptions of each machine—electricity, water, and wastewater—and an emission-factor-based method for calculating the carbon footprint is presented. Historical operational records then train a time-series model to predict carbon-footprint trends for the next twelve months. Because new stores or newly installed machines often lack sufficient data, a clustering model is designed to identify similar groups based on equipment usage patterns and environmental conditions; handling resources of different scales separately further enhances model stability and accuracy. The results show that both the time-series model and the clustering model achieve good accuracy. The framework can not only help the self-service laundry industry develop emission-reduction strategies but can also be extended to other capacitated service sectors—such as restaurants and EV charging stations—as a reference for sustainable operations. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-21T16:18:05Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-21T16:18:05Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 謝辭 i
論文摘要 ii THESIS ABSTRACT iii Content iv List of Tables vii List of Figures viii Chapter 1 Introduction 1 1.1 Background and Motivation 1 1.2 Research Objectives 3 1.3 Research Scope and Limitation 5 Chapter 2 Literature Review 7 2.1 Carbon Footprint 7 2.2 Time Series Analysis 9 2.3 Clustering 12 2.4 Evaluation Metrics 14 Chapter 3 Problem Description 15 3.1 Problem Description 15 3.2 Data Challenges for Carbon Footprint 16 3.3 Method 18 Chapter 4 Model Development 22 4.1 Global Data preparation 22 4.1.1 Defining Forecast Targets 22 4.1.2 Feature Definition and Selection 25 4.1.3 Preliminary Data Processing 27 4.2 Time-Series Model Building 28 4.2.1 Data Processing for Time-Series Models 29 4.2.2 Model Architecture 31 4.2.3 Model Evaluation 33 4.3 Two-Stage Clustering Model Building 35 4.3.1 Data Processing for Clustering 35 4.3.2 Two-stage Clustering Model Framework 36 4.3.3 Model Evaluation 40 4.4 Conclusion 41 Chapter 5 Experiment Results and Discussion 42 5.1 Data Configuration 43 5.1.1 Data Description 43 5.1.2 Preliminary Data Processing and Target-Variable Derivation 43 5.1.3 Features Overview 48 5.2 Time Series Forecasting Experiments 50 5.2.1 Experimental Setup 50 5.2.2 Time-Series Model Experiments 52 5.2.3 Results and Analysis 55 5.3 Two-Stage Forecasting Experiments 57 5.3.1 Experimental Setup 58 5.3.2 Two-Stage Clustering Model Experiments 60 5.3.3 Results and Analysis 62 5.4 Managerial Implications 67 Chapter 6 Conclusion 68 6.1 Conclusion 68 6.2 Future Work 69 Reference 70 Appendix A 76 Appendix B 77 | - |
| dc.language.iso | en | - |
| dc.subject | 碳足跡 | zh_TW |
| dc.subject | 容量限制服務業 | zh_TW |
| dc.subject | 物聯網 | zh_TW |
| dc.subject | 時間序列分析 | zh_TW |
| dc.subject | 分群模型 | zh_TW |
| dc.subject | capacitated service industry | en |
| dc.subject | carbon footprint | en |
| dc.subject | clustering model | en |
| dc.subject | time-series analysis | en |
| dc.subject | Internet of Things | en |
| dc.title | 基於深度學習的物聯網設備容量限制服務業之碳足跡排放預測 - 以自助洗衣業為例 | zh_TW |
| dc.title | Deep Learning-Based Prediction of Carbon Footprint Emissions for IoT-enabled Capacitated Service Industry | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 黃奎隆;蕭鉢 | zh_TW |
| dc.contributor.oralexamcommittee | Kwei-Long Huang;Bo Hsiao | en |
| dc.subject.keyword | 碳足跡,容量限制服務業,物聯網,時間序列分析,分群模型, | zh_TW |
| dc.subject.keyword | carbon footprint,capacitated service industry,Internet of Things,time-series analysis,clustering model, | en |
| dc.relation.page | 77 | - |
| dc.identifier.doi | 10.6342/NTU202503664 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2025-08-08 | - |
| dc.contributor.author-college | 管理學院 | - |
| dc.contributor.author-dept | 資訊管理學系 | - |
| dc.date.embargo-lift | 2030-08-04 | - |
| 顯示於系所單位： | 資訊管理學系 | |
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