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研究生: 高碩
Kao, Shuo
論文名稱: 區塊匹配應用於雲層移動估計之全天空影像與衛星雲圖日射量估計與預測系統
Cloud motion estimation based on block matching for solar irradiance estimation and prediction with all-sky images and satellite cloud images
指導教授: 呂藝光
Leu, Yih-Guang
口試委員: 呂藝光
Leu, Yih-Guang
吳政郎
Wu, Jenq-Lang
陶金旺
Tao, Chin-Wang
鄭錦聰
Jeng, Jin-Tsong
陳松雄
Chen, Song-Shyong
口試日期: 2024/07/30
學位類別: 碩士
Master
系所名稱: 電機工程學系
Department of Electrical Engineering
論文出版年: 2024
畢業學年度: 112
語文別: 中文
論文頁數: 115
中文關鍵詞: 衛星雲圖全天空影像粒子測速區塊匹配雙向長短期記憶
英文關鍵詞: satellite cloud images, all-sky images, particle image velocimetry block matching, bidirectional long short-term memory
DOI URL: http://doi.org/10.6345/NTNU202401627
論文種類: 學術論文
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  • 本文透過全天空影像及衛星雲圖的分析,來開發以影像中雲層為特徵的估計與預測系統。在全天空影像中,使用紅藍比例法擷取雲層特徵影像,並提出適應式閾值在不同背景亮度的情況下更精確地判定雲層資訊計算雲層在整張圖的占比,以及太陽周圍雲特徵分析,在衛星雲圖中使用超像素分割出觀測區域雲層並利用紅藍比例法擷取雲層資訊;透過粒子測速區塊匹配推估雲層移動情形,製作未來數分鐘至小時之雲層情況,提取雲層特徵,作為雙向長短期記憶模型之輸入,而模型輸出為日射量。使用三個評估指標來檢視模型學習情形,包含相對均方根誤差、相對平均絕對誤差與預測技巧比較估計及預測成果,其中估計與預測時長 120 分鐘實驗的相對平均絕對誤差分別可達 21.99%與 34.66%。

    This thesis develops irradiance estimation and prediction system using all-sky and satellite cloud images. In all sky images, the red-blue ratio method and adaptive thresholds are used to extract cloud features and analyze cloud coverage. Superpixel segmentation is applied to extract cloud features in satellite cloud images. Particle image velocimetry block matching is used to compute cloud motion. Cloud features are fed into a bidirectional long short-term memory model to forecast solar irradiance. Performance is evaluated using relative root mean square error, relative mean absolute error, and forecast skill metrics. The relative mean absolute erros of the forcasting with lead time of 2 hours and estimation are 34.66% and 21.99%, respectively.

    第一章 緒論 1 1.1研究動機與背景 1 1.2研究目的 2 1.3研究方法 2 1.4論文架構 2 第二章 文獻探討與回顧 4 2.1 雲層特徵擷取 4 2.2 雲層移動估計 5 2.3 日射量預測技術與評估 5 第三章 日射量估計系統設計 7 3.1日射量估計系統架構 8 3.2全天空影像 9 3.3黃道軌跡追蹤 10 3.3.1太陽時間 10 3.3.2地平坐標系中太陽位置 12 3.3.3地平座標轉換圖形座標 13 3.3.4類神經網路 14 3.4全天空影像雲層特徵擷取 16 3.4.1全天空影像處理流程 16 3.4.2建築物修正 17 3.4.3全天空影像紅藍比例法 17 3.4.4太陽像素去除 18 3.4.5中心區域加權 19 3.4.6太陽周圍區域雲層特徵 19 3.4.7適應式雲分割閾值 20 3.4.8全域雲層占比與雲層厚度特徵 21 3.5長短期記憶(Long Short-Term Memory, LSTM) 22 3.5.1雙向長短期記憶(Bi-Directional LSTM, BiLSTM) 23 3.6評估指標 24 第四章 日射量預測系統設計 25 4.1日射量預測之系統架構 26 4.2衛星雲圖 27 4.2.1可見光衛星雲圖 27 4.2.2真實色衛星雲圖 28 4.3衛星雲圖特徵擷取流程 29 4.3.1框選觀測區域 30 4.3.2 超像素分割 31 4.3.3 衛星雲圖紅藍比例法 32 4.4雲層運動估計 34 4.4.1 區塊匹配 34 4.4.2粒子測速區塊匹配 35 4.5製造預測影像 36 4.5.1 全天空預測影像製作流程 36 4.5.2 衛星雲圖預測影像製作流程 37 第五章 實驗設計與結果 38 5.1資料集描述 39 5.1.1全天空影像資料集 39 5.1.2衛星雲圖資料集 40 5.2適應式閾值分析 41 5.2.1日射量下降率與晴空率 41 5.2.2適應式閾值與固定式閾值回歸分析 42 5.2.3適應式閾值取樣率回歸分析 50 5.3 日射量估計 53 5.4 預測影像雲層相似性分析 57 5.4.1全天空影像全域相似性 58 5.4.2全天空影像太陽周圍區域相似性 60 5.4.3衛星雲圖臺灣地區像相似性 62 5.4.4衛星雲圖觀測區域像相似性 64 5.5 日射量預測 66 5.5.1 五分鐘間隔日射量預測 67 5.5.2 十分鐘間隔日射量預測 86 第六章 結論與未來展望 108 6.1結論 108 6.2未來展望 109 參 考 文 獻 110

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