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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 韓仁毓 | zh_TW |
| dc.contributor.advisor | Jen-Yu Han | en |
| dc.contributor.author | 洪瑞妤 | zh_TW |
| dc.contributor.author | Ruei-Yu Hong | en |
| dc.date.accessioned | 2025-07-21T16:05:47Z | - |
| dc.date.available | 2025-07-22 | - |
| dc.date.copyright | 2025-07-21 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-07-11 | - |
| dc.identifier.citation | Agbulut, U., Gürel, A. E., and Biçen, Y. (2021). Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison. Renewable Sustainable Energy Reviews, 135.
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Energy Conversion and Management, 235:113960. 何明錦, 黃國倉, and 建築工程(2013). 臺灣建築能源模擬解析用逐時標準氣象資料TMY3 之建置與研究. 內政部建築研究所. 經濟部能源局(2024). 我國再生能源發電統計彙編（至2023 年底）. 行政院國家發展委員會(2022). 臺灣2050 淨零排放路徑與策略總說明. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/97875 | - |
| dc.description.abstract | 本研究旨在建立高解析度的全天空日射量（Global Horizontal Irradiance, GHI）預測模型，以支援再生能源發展規劃與氣候變遷調適策略。在面對日益嚴峻的氣候變遷及能源需求挑戰下，精確且可靠的太陽能潛力評估已成為重要的研究課題。研究整合2012至2021年間之Himawari-8及MTSAT-2衛星觀測資料，透過模型進行日射量反演建構空間解析度為2公里之標準氣象年（Typical Meteorological Year, TMY）GHI資料集。透過臺北市與臺南市對比分析，研究指出兩地區之氣候特徵與日射量差異，顯示臺北市地區之GHI年際變異較大且季節波動明顯，而臺南市則具備較為穩定且豐富之太陽輻射資源。在方法上，研究納入日曆、氣象與空氣污染等四類共20項變數，並建構堆疊集成機器學習模型，以提升預測精度與解釋能力。結果顯示，Stacking模型相較單一模型在臺北市與臺南市的決定係數分別達0.95與0.97，誤差顯著降低。特徵重要性分析結果指出，臺北市之預測模型主要受如最大氣溫與降水量等動態氣象因子主導，其總貢獻度達77.6%；相較之下，臺南市氣象因子之貢獻度相對較低，而日曆因子之重要性則有所提升，此反映該地區更為規律之氣候模式。此外，透過極端氣候事件分析以凱米颱風為例，驗證TMY GHI作為穩定氣候參考因子價值，其能有效提高模型在極端情境下預測準確性。最後，本研究以CMIP6資料預測不同氣候情境下未來GHI變化，在低排放情境（SSP126）下，臺北市與臺南市日射量均呈增長趨勢；中排放情境（SSP245）中期出現區域差異，臺北市減少0.45%、臺南市增加0.14%，長期則趨於一致；高排放情境下，兩地日射量皆明顯下降，且臺北市減幅較早出現。本研究結合標準氣象年之衛星資料與多源因子以建構堆疊集成學習模型，有效提升日射量預測精確性與可解釋性，並驗證標準氣象年在極端氣候下的參考價值，以及不同氣候情境下之預測亦揭示區域差異，突顯因地制宜能源規劃的重要性，望可作為再生能源發展與氣候韌性社會建構之科學基礎。 | zh_TW |
| dc.description.abstract | This study develops a high-resolution Global Horizontal Irradiance (GHI) prediction model to support renewable energy planning and climate adaptation. By integrating Himawari-8 and MTSAT-2 satellite data (2012–2021), a 2 km Typical Meteorological Year (TMY) GHI dataset was constructed. A comparison between Taipei and Tainan revealed distinct regional solar patterns—Taipei showed higher interannual variability, while Tainan had more stable irradiance. The model incorporates 20 features across calendar, meteorological, and air pollution categories, using a stacking ensemble approach to enhance accuracy and interpretability. Results show R² values of 0.95 (Taipei) and 0.97 (Tainan), with significantly reduced RMSE and MAE. Feature importance analysis indicated Taipei's predictions were affected by dynamic weather factors, while Tainan relied more on calendar-based variables. Typhoon Gaemi analysis further validated the TMY GHI's role in improving model robustness under extreme events. Climate scenario projections based on CMIP6 suggest rising GHI under SSP126, regional divergence under SSP245, and declines under SSP585, with Taipei affected earlier. This study highlights the importance of integrating high-resolution data and machine learning for reliable solar potential forecasting and supports the development of climate-resilient energy strategies. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-07-21T16:05:47Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-07-21T16:05:47Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 致謝 ii
摘要 iii Abstract iv 目次 v 圖次 vii 表次 ix 第一章緒論 1 1.1 研究背景 1 1.2 研究動機與目的 3 1.3 研究流程 4 1.4 論文架構 4 第二章文獻回顧 6 2.1 全球能源轉型與韌性挑戰 6 2.2 太陽輻射測量與獲取之方法 8 2.3 全天空日射量時空變異與環境因子影響分析 10 2.4 太陽輻射潛能預測方法之演進與發展 13 2.5 標準氣象年資料於太陽輻射分析之應用現況與挑戰 15 2.6 小結 16 第三章研究方法 18 3.1 資料收集與前處理 20 3.1.1 標準氣象年所建構之全天空日射量 20 3.1.2 日曆時間特徵與週期性函數轉換 24 3.1.3 氣象因子數據整合與空間一致性處理 25 3.1.4 空氣污染因子時空數據處理與品質控制 27 3.2 太陽輻射潛能預測模型 31 3.2.1 第一層基礎模型：四種機器學習演算法構建 32 3.2.2 第二層融合模型：堆疊架構下的權重學習與整合策略 34 3.3 模型性能評估與SHAP 特徵重要性分析 35 3.3.1 預測精度評估與模型性能量化指標 36 3.3.2 SHAP 特徵重要性分析 37 3.4 氣候變遷情境下太陽輻射潛能變異性評估 38 3.5 小結 40 第四章實驗結果與分析42 4.1 研究區域 42 4.2 標準氣象年之全天空日射量建構方法與特性評估 43 4.2.1 代表月份選取與代表性驗證 44 4.2.2 累積分布函數之比較分析 45 4.2.3 全年尺度標準氣象年GHI 之統計分析 47 4.3 太陽輻射潛能預測模型之建構與評估 49 4.3.1 模型綜合性能之比較與分析 49 4.3.2 SHAP 特徵重要性分析 53 4.3.3 TMYGHI 在極端氣候條件下提升模型穩健性之效益評估 56 4.4 氣候變遷情境下太陽輻射潛能變異性之分析評估 58 4.4.1 不同排放情境下GHI 之未來變化趨勢分析 58 4.4.2 氣候變遷驅動因子與GHI 變化之物理機制分析 60 4.4.3 太陽輻射潛能區域異質性分析與氣候適應策略 66 第五章結論與建議 68 5.1 結論 68 5.2 建議與未來工作 72 參考文獻 73 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 多源異質數據 | zh_TW |
| dc.subject | 標準氣象年 | zh_TW |
| dc.subject | 全天空日射量 | zh_TW |
| dc.subject | 集成學習 | zh_TW |
| dc.subject | 標準氣象年 | zh_TW |
| dc.subject | 全天空日射量 | zh_TW |
| dc.subject | 集成學習 | zh_TW |
| dc.subject | 多源異質數據 | zh_TW |
| dc.subject | Ensemble Learning | en |
| dc.subject | Multi-Source Heterogeneous Data | en |
| dc.subject | Global Horizontal Irradiance | en |
| dc.subject | Typical Meteorological Year | en |
| dc.subject | Multi-Source Heterogeneous Data | en |
| dc.subject | Ensemble Learning | en |
| dc.subject | Global Horizontal Irradiance | en |
| dc.subject | Typical Meteorological Year | en |
| dc.title | 整合標準氣象年資料與多模型融合技術於太陽輻射潛能預測 | zh_TW |
| dc.title | Integration of Typical Meteorological Year Data and Multi-Model Fusion Techniques for Forecasting Solar Irradiance Potential | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 葉大綱;楊明德 | zh_TW |
| dc.contributor.oralexamcommittee | Ta-Kang Yeh;Ming-Der Yang | en |
| dc.subject.keyword | 標準氣象年,全天空日射量,集成學習,多源異質數據, | zh_TW |
| dc.subject.keyword | Typical Meteorological Year,Global Horizontal Irradiance,Ensemble Learning,Multi-Source Heterogeneous Data, | en |
| dc.relation.page | 79 | - |
| dc.identifier.doi | 10.6342/NTU202500940 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2025-07-14 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| dc.date.embargo-lift | 2030-07-11 | - |
| 顯示於系所單位： | 土木工程學系 | |
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