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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 張斐章 | zh_TW |
dc.contributor.advisor | FiJohn Chang | en |
dc.contributor.author | 陳筑涵 | zh_TW |
dc.contributor.author | Chu-Han Chen | en |
dc.date.accessioned | 2024-09-18T16:26:00Z | - |
dc.date.available | 2024-09-19 | - |
dc.date.copyright | 2024-09-18 | - |
dc.date.issued | 2024 | - |
dc.date.submitted | 2024-08-08 | - |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/95863 | - |
dc.description.abstract | 近年來氣候變遷的影響,傳統養殖漁業面對氣候風險提高,開發新型永續的養殖模式有望降低養殖風險,本研究目的為探討在面對氣候風險的衝擊下,使用漁電共生系統相較傳統養殖模式是否能促進其調適能力、增加韌性,同時具低碳生產的潛力。研究區域位於雲林縣台西鄉之海水養殖研究中心,針對文蛤養殖的傳統模式及漁電共生系統,以系統動態技術分別建構水-能源-文蛤生產-土地-氣候鏈結模型,並透過機器學習進行模式優化,分析在氣候變遷影響下,兩者的資源使用及經濟效益差異。研究結果顯示相較於傳統養殖,漁電共生40%遮蔽率的太陽能板可以提供養殖池遮蔽功能,降低陽光直射到水體的程度,水溫平均低2.5°C,且水資源總使用效益顯著提升30%;所產生之綠電可替代燃煤發電,降低溫室氣體的產生;雖然文蛤總產量下降27%,但綠電收益,增加漁民收入來源,可以彌補文蛤產量損失收益。本研究進一步探討全球暖化對文蛤養殖產業的潛在影響,針對傳統養殖以及漁電共生模型進行敏感度分析,比較在室外氣溫上升0.5℃和1℃時,這兩個模型的變異性,結果顯示若氣溫上升1°C,為確保文蛤的適宜生長環境,需更多的海水源以調節水溫及鹽度,水資源消耗急遽上升,需更有效的水資源操作管理。本研究透過不同太陽能板的遮蔽率設置,模擬了遮蔽率從0%至70%對文蛤總收成產量和發電量的影響。結果顯示當太陽能板遮蔽率設定在45%左右時,文蛤的產量將會逐漸低於7成,表示在增設太陽能板時,需要在糧食產量和發電量之間取得平衡,以提高產業的永續性。整體而言,漁電共生的綠能發電以及資源使用效益提升,提供養殖漁業邁向良好的低碳轉型機會,是極具低碳潛力的新穎養殖系統,對產業及環境永續發展具正面效益。 | zh_TW |
dc.description.abstract | In recent years, due to the impact of climate change, traditional aquaculture fisheries have faced increased climate risks. The development of new sustainable aquaculture models is expected to reduce these risks. The purpose of this study is to explore the use of fish and electricity symbiosis systems compared with traditional ones in the face of climate risks. The study aims to determine whether this breeding model can promote adaptability, increase resilience, and have the potential for low-carbon production. This study explores whether using aquavoltaic systems, compared to traditional aquaculture methods, can enhance adaptation capacity, increase resilience, and promote low-carbon production potential in the face of climate risks. The study area is located at the Mariculture Research Center Taihsi Station, Yunlin County, focusing on the traditional and aquavoltaic systems of clam farming. We constructed models linking water, energy, clam production, land, and climate using system dynamics techniques. Then, we optimized these models with machine learning to analyze the differences in resource usage and economic benefits under the influence of climate change.
The results show that, compared to traditional aquaculture, the aquavoltaic system with 40% shading provided by solar panels reduces direct sunlight exposure to the water, lowering the average water temperature by 2.5°C and significantly enhancing water resource utilization efficiency by 30%. The green electricity generated can replace coal-fired power, reducing greenhouse gas emissions. Although the total clam yield decreased by 27%, the income from green electricity compensates for the loss in clam yield, providing an additional revenue stream for fishers. This study further explores the potential impact of global warming on the hard clam aquaculture industry by conducting sensitivity analyses on both traditional and aquavoltaic systems. It compares the variability of these two models when outdoor temperatures rise by 0.5°C and 1°C. The results show that if the temperature increases by 1°C, more seawater is needed to regulate water temperature and salinity to ensure a suitable growth environment for hard clams. Consequently, water resource consumption increases significantly, necessitating more effective management. This study simulated the impact of solar panel shading rates ranging from 0% to 70% on total clam yield and power generation. The results indicate that when the shading rate is set at around 45%, clam yield gradually drops below 70%, suggesting that a balance between food production and power generation needs to be struck to enhance sustainability. Overall, the aquavoltaic system's green energy production and improved resource use efficiency present a promising opportunity for low-carbon transformation in aquaculture, contributing positively to the sustainable development of both industry and the environment. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-09-18T16:26:00Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2024-09-18T16:26:00Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 口試委員審定書 I
謝辭 II 中文摘要 IV Abstract V 目次 VII 圖次 IX 表次 XI 第一章 前言 1 1.1 研究動機 1 1.2 研究目的 2 1.3 研究架構 2 第二章 文獻回顧 4 2.1 漁電共生 4 2.1.1 漁電共生發展現況 4 2.1.2 漁電共生之案例 5 2.2 文蛤養殖 6 2.3 水-能源-糧食-氣候-土地鏈結之研究 (Water-Energy-Food-Climate-Land Nexus) 8 2.4 系統動態學之相關研究 10 2.5 機器學習之相關研究 11 2.5.1 參數最佳化 11 2.5.2 模擬以及推估之應用 12 第三章 理論概述 13 3.1 系統動態學 (System Dynamics, SD) 13 3.2 類神經網路 (Artificial Neural Networks, ANNs) 15 3.3 倒傳遞神經網路 (Back-propagation Neural Network, BPNN) 16 3.4 遺傳演算法 (Genetic Algorithm, GA) 19 3.5 模式評估指標 24 3.5.1 決定係數(Coefficient of Determination, R2) 24 3.5.2 均方根誤差(Root-Mean-Square Error, RMSE) 25 3.5.3 平均絕對百分比誤差(Mean Absolute Percentage Error, MAPE) 25 第四章 研究案例 27 4.1 研究區域 27 4.2 資料收集 28 4.2.1 氣象站資料 28 4.2.2 養殖池之水質資料 29 4.2.3 養殖池之文蛤成長資料 29 4.2.4 資料處理 29 4.3 研究流程 30 4.4 模式設定 32 4.4.1 文蛤在傳統養殖及漁電共生系統下之W-E-F-C-L Nexus系統動態模型 32 4.4.2 遺傳演算法模式之基本設定 44 4.4.3 傳遞神經網路之基本設定 46 第五章 結果與討論 47 5.1 系統動態結合遺傳演算法之模擬結果 47 5.1.1 水溫模擬之最佳參數解 47 5.1.2 溶氧量模擬之最佳參數解 52 5.2 BPNN模式提升結果 57 5.2.1 水溫模擬之設定與結果 57 5.2.2 溶氧量模擬之設定與結果 62 5.2.3 模式優化結果 64 5.3 各資源因果鏈結 67 5.3.1 氣候鏈結 67 5.3.2 水資源鏈結 68 5.3.3 能源鏈結 69 5.3.4 糧食鏈結 70 5.3.5 土地鏈結 72 5.4 敏感度分析 74 5.5 太陽能板之情境分析 79 第六章 結論與建議 84 6.1 結論 84 6.1.1 模型之模擬結果 84 6.1.2 各鏈結之效益 84 6.1.3 漁電共生之低碳潛力 85 6.2 建議 86 參考文獻 87 附錄 91 | - |
dc.language.iso | zh_TW | - |
dc.title | 結合系統動態與機器學習模型建立水-能源-糧食-氣候-土地鏈結於漁電共生 | zh_TW |
dc.title | Integrating System Dynamics and Machine Learning to Establish the Water-Energy-Food-Climate-Land (WEFCL) Nexus of Aquavoltaics | en |
dc.type | Thesis | - |
dc.date.schoolyear | 112-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 張麗秋;鄭舒婷;黃文政;周昱翰 | zh_TW |
dc.contributor.oralexamcommittee | LiChiu Chang;SuTing Cheng;WenCheng Huang;YuHan Chou | en |
dc.subject.keyword | 水-能源-糧食-氣候-土地鏈結,文蛤,系統動態,機器學習,漁電共生, | zh_TW |
dc.subject.keyword | Water-Energy-Food-Climate-Land Nexus(W-E-F-C-L Nexus),Clam,System Dynamics,Machine learning,Aquavoltaic Systems, | en |
dc.relation.page | 92 | - |
dc.identifier.doi | 10.6342/NTU202403913 | - |
dc.rights.note | 同意授權(限校園內公開) | - |
dc.date.accepted | 2024-08-12 | - |
dc.contributor.author-college | 生物資源暨農學院 | - |
dc.contributor.author-dept | 生物環境系統工程學系 | - |
dc.date.embargo-lift | 2026-08-07 | - |
顯示於系所單位: | 生物環境系統工程學系 |
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