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研究生: 郭家宏
Guo, Jia-Hong
論文名稱: 基於超像素分割之衛星雲圖進行預測與估計日射量之系統
A system for predicting and estimating solar irradiance based on satellite cloud image with superpixel segmentation
指導教授: 呂藝光
Leu, Yih-Guang
口試委員: 鄭錦聰
Jeng, Jin-Tsong
吳政郎
Wu, Jenq-Lang
陶金旺
Tao, Chin-Wang
莊鎮嘉
Chuang, Chen-Chia
呂藝光
Leu, Yih-Guang
口試日期: 2022/07/18
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: 2022
畢業學年度: 110
語文別: 中文
論文頁數: 101
中文關鍵詞: 日射量衛星雲圖超像素分割光流法長短期記憶
英文關鍵詞: solar irradiance, satellite cloud image, super pixel segmentation, optical flow, LSTM
研究方法: 實驗設計法比較研究觀察研究文件分析法
DOI URL: http://doi.org/10.6345/NTNU202201314
論文種類: 學術論文
相關次數: 點閱:88下載:113
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  • 由於日射量容易受天氣因素影響而容易產生變化,進而造成太陽能發電量不穩定,因此,難以將其整合入區域電網當中。本文建立一個以超像素分割衛星雲圖為基礎之日射量估計與預測系統。分析衛星雲圖並萃取其雲層特徵,採用光流法,分析雲層運動,生成預測的衛星雲圖。再將這些影像特徵與一些天氣預報特徵作為長短期記憶(LSTM)之輸入,進行日射量的估計與預測。本文使用幾個效能指標來評估估計與預測的效果,包括平均絕對誤差(MAE)、均方根誤差(RMSE)以及判定係數(R^2);並設計數個實驗方法進行比較,實驗結果顯示,本文所提出方法有達到預期的成果。

    Since solar radiation is susceptible to changes due to weather factors, it is difficult to integrate it into the regional power grid because of the instability of solar power generation. In this study, a solar irradiance estimation system based on satellite cloud image superpixel segmentation was established. The satellite cloud image is analyzed, the cloud features are extracted, the satellite cloud image is used as the input, the cloud layer movement is analyzed by the optical flow method, and the predicted satellite cloud image is generated. These features are used as input to long short-term memory (LSTM) to estimate and predict solar irradiance. Several performance metrics are used to evaluate the estimation and prediction, including MAE, RMSE, and R2. Several methods are compared, and the experimental results show that the proposed method performs better.

    誌  謝 I 目 錄 IV 表目錄 VII 圖目錄 IX 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雲層特徵提取 4 2.1.1機器學習 4 2.1.2圖像處理 4 2.2雲層移動預測 5 2.3日射量估計與預測 6 第三章研究方法 8 3.1衛星雲圖 8 3.1.1可見光衛星雲圖 8 3.1.2真實色衛星雲圖 9 3.2色彩空間轉換 10 3.3超像素分割 11 3.3.1 SLIC超像素分割 11 3.3.2改進型SLIC超像素分割 13 3.4紅藍比例法 14 3.5太陽軌道物理模型 15 3.5.1本地標準時間子午線(Local Standard Time Meridian, Tlstm) 15 3.5.2天文時間差(Equation of Time, EoT) 15 3.5.3時間修正因子(Time Correction Factor, TC) 15 3.5.4真太陽時間(Local Solar Time, LST) 16 3.5.5時角(Hour Angle, HA) 16 3.5.6太陽偏差角(Declination, 𝛅solar) 16 3.5.7太陽高度角(Elevation) 16 3.5.8太陽方位角(Azimuth) 17 3.6光流法 17 3.6.1 Lucas-Kanade 光流法 18 3.6.2光流場(Optical Field) 19 3.7長短期記憶(LONG SHORT-TERM MEMORY , LSTM) 20 3.8周期特徵編碼(ENCODING CYCLICAL CONTINUOUS FEATURES) 22 第四章日射量估計與預測之系統架構 24 4.1日射量估計與預測架構 24 4.2雲層特徵萃取 26 4.2.1改進型紅藍比例法 26 4.2.2框選區域 28 4.2.3改進型SLIC分割區域 29 4.2.4雲層特徵 30 4.3預測影像 31 4.3.1光流法預測下一張衛星雲圖 33 4.3.2平均移動量與剪裁向量 33 4.3.3後處理 47 第五章效能評估指標與實驗結果 50 5.1 資料集 50 5.2評估指標 52 5.3日射量估 52 5.3.1實驗一 沒有衛星雲圖的特徵 52 5.3.2實驗二 加入衛星雲圖的特徵 56 5.4日射量預測 58 5.4.1實驗三 沒有衛星雲圖的特徵 58 5.4.2實驗四 加入衛星雲圖的特徵 77 5.4.3實驗五 加入預測的衛星雲圖特徵 81 5.4.3實驗六 比較30組的預測誤差 86 第六章結論與未來展望 94 6.1結論 94 6.2未來展望 95 參 考 文 獻 97

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