深度學習模型於桌上型電腦零件碳排預測之研究

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姓名

吳浩瑋(Hao-Wei Wu) 查詢紙本館藏

畢業系所

資訊管理學系在職專班

論文名稱

深度學習模型於桌上型電腦零件碳排預測之研究
(A Study on Predicting Carbon Emissions of Desktop Computer Components Using Deep Learning Models)

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至系統瀏覽論文 (2030-7-1以後開放)

摘要(中)

面對全球永續與碳中和目標，企業急需於產品設計階段導入低碳策略。桌上型電腦作為高碳電子產品，其零組件在材料與製造等環節皆會影響碳排放，而現行碳排放大多於生產後評估，無法於設計初期即時預測。
本研究提出一套深度學習預測模型，結合自編碼器進行特徵選取、類神經網路進行碳排放回歸預測，並導入知識蒸餾以強化模型精準度與效能。研究建立桌上型電腦零組件碳排放資料集，並於高效能、商務型與輕量型產品中進行模型驗證。
實驗結果顯示，該模型可於設計初期有效預測各組件碳排放，協助企業優化設計並落實低碳設計目標，對電子產業永續發展具有高度實務應用價值。

摘要(英)

In the face of global sustainability and carbon neutrality goals, enterprises urgently need to implement low-carbon strategies during the product design stage. Desktop computers, as high-carbon electronic products, generate emissions throughout material selection and manufacturing processes. However, current carbon footprint assessments are mostly conducted post-production, lacking the ability to predict emissions early in the design phase.
This study proposes a deep learning-based predictive model that integrates autoencoders for feature selection, neural networks for carbon emission regression, and knowledge distillation to enhance model accuracy and performance. A carbon emission dataset of desktop computer components was constructed and the model was validated across high-performance, business, and lightweight product categories.
Experimental results show that the proposed model can effectively predict component-level carbon emissions during the early design phase, assisting enterprises in optimizing product design and achieving low-carbon goals. This approach provides strong practical value for promoting sustainability in the electronics industry.

關鍵字(中)

★ 深度學習
★ 碳排放預測
★ 類神經網路
★ 知識蒸餾
★ ESG永續發展

關鍵字(英)

★ Deep Learning
★ Carbon Emission Prediction
★ Neural Network
★ Knowledge Distillation
★ ESG Sustainability

論文目次

摘要 i
ABSTRACT ii
致謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
1 第一章緒論 1
1.1 研究背景 1
1.2 研究動機與目的 4
1.3 研究貢獻 7
1.4 論文流程與架構 10
2 第二章文獻探討 12
2.1 碳排放預測與對企業的影響 14
2.2 神經網路（NN）在碳排放預測的應用 15
2.3 知識蒸餾（KD）在機器學習與回歸問題的應用 19
3 第三章研究方法 24
3.1 資料轉換與前處理 25
3.2 模型架構 26
3.2.1 特徵選取(Autoencoder) 27
3.2.2 神經網路(Neural Network) 30
3.2.3 知識蒸餾(Knowledge Distillation) 33
4 第四章實驗結果與分析 36
4.1 資料集與前置處理 36
4.2 比較模型 39
4.3 模型效能驗證 – Teacher Model（全部專案） 43
4.4 模型效能驗證 - NNCKD（個別專案） 45
4.5 知識蒸餾參數（Alpha、Temperature） 50
4.6 參數設定探討（Epoch、批次大小、Learning rate） 54
4.7 消融實驗 58
4.8 實例驗證 60
5 第五章結論與未來研究方向 63
5.1 研究結論 63
5.2 研究限制 64
5.3 未來研究方向 64
6 參考文獻 66

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指導教授

陳以錚(Yi-Cheng Chen)

審核日期

2025-6-25

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