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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 | 莫詒雯 | zh_TW |
| dc.contributor.author | Yi-Wen Mo | en |
| dc.date.accessioned | 2023-08-15T16:40:07Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-08-15 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-07-31 | - |
| dc.identifier.citation | Almorox, J., & Hontoria, C. (2004). Global solar radiation estimation using sunshine duration in Spain. Energy Conversion and Management, 45(9-10), 1529-1535.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88520 | - |
| dc.description.abstract | 在現今全球環境下，為解決資源匱乏及減少溫室氣體的排放，各國皆致力於推廣綠色能源的發展。在各種綠色能源中，太陽能相對於其他綠能，擁有較低的發展門檻以及能夠減少全球每年約25%的溫室氣體排放量，而綠色能源又以太陽能發展為主。太陽能發電效益主要取決於地面所接收到的太陽輻射量，因此獲取準確的太陽輻射數據，除了可作為太陽光電選址的參考依據，也在制定未來能源政策時扮演重要的角色。現今有關獲取太陽輻射量之中央氣象局(Central Weather Bureau, CWB)地面測站會隨著時間折舊及天氣影響，致使數據缺失或有異常的情況。另外，對於沒有地面測站的區域來說，面臨著難以直接獲取太陽輻射數據的困境。據前所述，本研究結合衛星影像與地面測站，透過機器學習建立全域與區域太陽輻射模型，同時探討太陽輻射量影響因子，透過分析單一測站及區域測站模型中的變數重要性，說明各個變數對太陽輻射的貢獻程度。此外，本研究納入空間結構分析(Spatial Structure Analysis)，透過變異圖(Variogram)探討測站在空間上的相關性，並根據獲得的距離(Range)建立區域太陽輻射模型，將該模型與不考慮空間相關性的全域模型比較，兩者模型差異僅介於1至2\ W/m^2之間，表示空間相關性對於估計地面太陽輻射並非為關鍵因素。本研究除了驗證衛星影像太陽輻射量能夠作為與地面測站相互補之資訊外，也整合多元數據獲取重要影響因子，所建立的全域模型能提升約30%之估計精度，有助於各地區獲取太陽輻射數據，對於未來發展太陽能上可作為重要參考依據，並且優化臺灣太陽輻射量之資料庫助於相關能源政策之發展。 | zh_TW |
| dc.description.abstract | In the present era, countries are increasingly focusing on promoting the development of green energy to combat resource scarcity and reduce greenhouse gas emissions. Among the various renewable energy sources, solar energy has gained immense popularity due to its potential to reduce greenhouse gas emissions by up to 25%. Measuring solar radiation accurately is crucial for effective solar power generation. But equipment damage and weather changes make it hard to gather reliable data from areas without ground stations. To address this issue, this study utilizes a combination of satellite imagery and ground stations that are based on machine learning techniques to create solar radiation models on a global and regional scale. The study examines the various factors that impact solar radiation and their significance in single and regional models. The study examined station spatial correlation through the Variogram and established a regional solar radiation model. Comparison with a global model showed only a 1-2 W/m^2 difference, suggesting spatial correlation is not a significant factor in estimating ground solar radiation. This research confirms that satellite images are useful in addition to ground stations for measuring solar radiation. It also utilizes multivariate data to identify key factors that impact solar radiation levels. The resulting global model significantly augments the precision of solar radiation estimation by around 30%. This achievement is significant in providing reliable solar radiation information to different areas, serving as an essential reference for future solar energy development. Furthermore, enhancing Taiwan's solar radiation database can aid in the improvement of relevant energy policies. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-15T16:40:07Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-15T16:40:07Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 摘要 ii Abstract iii 目錄 iv 圖目錄 vi 表目錄 viii 第一章 前言 1 1.1 研究背景 1 1.2 研究動機與目的 2 1.3 研究流程 3 1.4 論文架構 4 第二章 文獻回顧 5 2.1 太陽輻射量影響因子 5 2.2 太陽輻射估計方法 7 2.2.1 太陽輻射量測儀器 7 2.2.2 太陽輻射模型 9 2.3 機器學習應用於太陽輻射量之研究整理 13 2.4 小結 14 第三章 研究方法 15 3.1 衛星影像輻射量與影響因子數據處理 16 3.2 太陽輻射模型建構 18 3.2.1機器學習演算法選擇與特性 18 3.2.2 模型輸入變數特徵選取 22 3.3 測站太陽輻射量空間結構分析 23 3.3.1 太陽輻射量變異圖 23 3.3.2 測站太陽輻射量變異圖計算過程 25 3.4 小結 25 第四章 實驗成果與討論 26 4.1 研究區域與資料集介紹 26 4.2 特徵選取分析成果 34 4.3 空間結構分析應用至區域模型建置 36 4.4 全域與區域太陽輻射模型建置分析成果 38 4.5 全域模型應用至太陽輻射估計 43 4.6 小結 54 第五章 結論與未來規劃 55 5.1 結論 55 5.2 未來工作建議 56 參考文獻 57 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 太陽輻射量估計 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 衛星影像 | zh_TW |
| dc.subject | 特徵選取 | zh_TW |
| dc.subject | 空間結構分析 | zh_TW |
| dc.subject | Satellite Image | en |
| dc.subject | Machine Learning (ML) | en |
| dc.subject | Feature Selection | en |
| dc.subject | Solar Radiation Estimation | en |
| dc.subject | Spatial Structure Analysis | en |
| dc.title | 基於機器學習與多元資料於太陽輻射量之估計 | zh_TW |
| dc.title | Estimation of Solar Radiation Based on Machine Learning Techniques and Multivariate Data | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 甯方璽;莊子毅 | zh_TW |
| dc.contributor.oralexamcommittee | Fang-Shii Ning;Tzu-Yi Chuang | en |
| dc.subject.keyword | 太陽輻射量估計,衛星影像,機器學習,特徵選取,空間結構分析, | zh_TW |
| dc.subject.keyword | Solar Radiation Estimation,Satellite Image,Machine Learning (ML),Feature Selection,Spatial Structure Analysis, | en |
| dc.relation.page | 61 | - |
| dc.identifier.doi | 10.6342/NTU202302454 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2023-08-02 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| 顯示於系所單位： | 土木工程學系 | |
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