博碩士論文 109353004 詳細資訊




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姓名 傅國唐(Kuo-Tang Fu)  查詢紙本館藏   畢業系所 機械工程學系在職專班
論文名稱 複合式類神經網路預測貨櫃船主機油耗
(Prediction of Main Engine Fuel Consumption of Container Ships Based on Hybrid Artificial Neural Network)
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摘要(中) 2023年起,國際航線船舶必須符合國際海事組織2023年EEXI(現有船舶能效指標)和CII(碳強度指標)要求,這些要求旨在大幅降低碳排放總量和碳排放強度。海事組織進一步制定戰略,要求船舶在2030年與2008年相比要減少40%的碳排放強度,到2050年更進一步要減少70%。因此,船舶運營商目前正在尋求可能的解決方案,以實現這一排放目標。在2030年之前,大多數船隻可通過優化船體設計和船舶速度調整(操作)方案進行碳排放強度的改善。而船速調整必須仰賴主機在各種船速與運行條件下的精準油耗預測結果,本研究是採用複合式類神經網路來預測主機油耗,與傳統上按照海試結果所繪製的船速油耗表相比,或是採用單一類神經網路所預測的油耗相比,採用複合式類神經網路可依照五種不同模型的預測結果設定適當配比,避免由於單一模型預測失準造成偏差。
摘要(英) By 2023, vessels in international trade must comply with IMO (International Maritime Organization) EEXI (Energy Efficiency Index for Existing Ships) and CII (Carbon Intensity Index) requirements which are aiming at a significant reduction of both total carbon emission and carbon emission intensity. IMO has further set up a strategy that will request vessels to reduce carbon intensity by 40% in 2030 and total emission by 70% in 2050 compared to the 2008 baseline. Therefore, vessel operators are now seeking possible solutions that could achieve this emission goal. Before 2030, most vessels are improving optimized hull design and speed adjustment (operation). Speed adjustment should be based on the precise prediction of main engine fuel oil consumption under different speeds and engine working conditions. By applying the 5-model hybrid artificial neural network and an adequate proportion of each model in the mixture, one can reduce residuals in fuel oil consumption (FOC) prediction in comparison with the FOC-speed table obtained from sea trial or normal single model prediction.
關鍵字(中) ★ 氣候變遷
★ 碳排放
★ 複合式類神經網路
關鍵字(英) ★ Global Warming
★ Carbon Emission
★ hybrid artificial neural network
論文目次 摘 要 iii
Abstract iv
致 謝 v
目 錄 vi
圖目錄 ix
表目錄 xii
符號說明 xiv
第一章 緒論 1
1-1 前言 1
1-1-1 聯合國氣候峰會(COP) 1
1-1-2 國際海事組織溫室氣體初始戰略 2
1-1-3 歐盟碳排放交易體系 6
1-2 研究動機與目的 7
1-3 研究流程 12
第二章 文獻回顧 13
第三章 類神經網路架構與原理 25
3-1 類神經網路介紹 25
3-1-1 類神經的概念 25
3-1-2 正向傳播 28
3-1-3 損失函數 29
3-2 反向傳播法 31
3-3 機器學習網路架構 37
3-3-1 感知器 38
3-3-2 多元線性迴歸 38
3-3-3 饋神經網路 40
3-3-4 支援向量機 40
3-3-5 徑向基函數 50
3-3-6 深度前饋神經網路 52
3-3-7 卷積神經網路 55
第四章 以複合式類神經網路預測主機燃油消耗量之實作 57
4-1 資料蒐集 58
4-2 資料預處理 78
4-3 模型建構與訓練 92
4-3-1 模型挑選 92
4-3-2 標準化 94
4-3-3 k折驗證 97
4-3-4 超參數選定 98
4-3-5 模型訓練 101
4-3-6 模型評估 102
4-3-7 模型調配 102
4-4 模型訓練結果 106
4-4-1 SVM模型實驗結果: 106
4-4-2 LASSO模型實驗結果: 108
4-4-3 Ridge模型實驗結果: 110
4-4-4 ElasticNet模型實驗結果: 112
4-4-5 Stacking模型實驗結果: 114
4-5 模型混合結果 116
第五章 結論與未來展望 121
5-1 結論 121
5-2 未來展望 122
參考文獻 123
附錄一 實際執行步驟與程式說明 125
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指導教授 李天錫 傅尹坤(Tien-Hsi Lee Yiin-Kuen Fuh) 審核日期 2022-7-11
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