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研究生： 朱財平
Chu, Tsai-Ping
論文名稱： 利用全天空影像分析進行日射量及太陽能發電量之估算與預測
All-Sky Image Analysis for Solar Irradiance and Solar Power Estimation and Prediction
指導教授： 呂藝光
Leu, Yih-Guang
口試委員： 曹昭陽 莊鎮嘉 鄭錦聰 陶金旺
口試日期： 2021/07/30
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： 2021
畢業學年度： 109
語文別： 英文
論文頁數： 90
中文關鍵詞： 日射量 、太陽能發電量 、全天空影像 、光流法 、長短期記憶
英文關鍵詞： solar irradiance, solar power, all-sky image, optical flow, LSTM
研究方法: 實驗設計法
DOI URL： http://doi.org/10.6345/NTNU202101168
論文種類： 學術論文
相關次數： 點閱：144  下載：18 
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    • 日射量容易受天氣因素影響而產生突坡事件，並造成太陽能發電量不穩定，以致難以將其整合入區域電網當中。本研究以全天空影像儀捕獲白晝之全天空影像，並建立一以影像特徵為基礎之日射量及太陽能發電量估算與預測系統。分析當前空影像以萃取全域及局部雲層之權重；利用光流法推算雲層移動狀態，並製作一分鐘之天空預測圖像。將雲層權重作為長短期記憶(LSTM)之輸入，以進行日射量估算與預測。並且，製作功率曲線，以進一步估算太陽能之發電量。本研究使用多個效能指標評估效果，包括平均絕對誤差(MAE)、均方根誤差(RMSE)、正歸化均方根誤差(nRMSE)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，本研究所提出方法具有較佳的性能。

    The ramp event of solar irradiance is prone to occur due to atmospheric conditions, and makes it difficult to integrate solar power into regional power grid. In this study, all-sky images are captured by the all-sky imager. A system is established to perform solar irradiance and solar power estimation and prediction based on image features. The global and the region cloud weights are extracted by analyzing the images. The predicted images for the next minute are made after the cloud movements are derived by using the optical flow. The long short-term memory (LSTM) is used as a training model with cloud weights as inputs for the solar irradiance estimation and prediction. Furthermore, power curves are made to estimate the solar power output. Several performance indices are used to evaluate the performance, including MAE, RMSE, nRMSE and R^2. Several methods are compared, and the experimental results show that the performance of the proposed method is better.

    Acknowledgements i Abstract in Chinese ii Abstract in English iii Table of Contents iv List of Tables vii List of Figures x Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Purpose 3 1.3 Methods 3 1.4 Framework 3 Chapter 2 Literature Review 5 2.1 Cloud Feature Extraction 5 2.2 Cloud Motion Detection 6 2.3 Solar Irradiance Estimation and Prediction 7 Chapter 3 Basic Principles 11 3.1 The Sun Track Physical Model 11 3.2 Optical Flow Method 14 3.2.1 Optical Flow 14 3.2.2 Lucas-Kanade Optical Flow 15 3.2.3 Optical Field 17 3.3 Long Short-Term Memory 17 3.3.1 Recurrent Neural Networks 17 3.3.1.1 Forward propagation 18 3.3.1.2 Backward propagation 19 3.3.1.3 Vanishing/ Exploding gradient 20 3.3.2 Long Short-Term Memory 21 Chapter 4 Proposed Methods 24 4.1 System Structure 24 4.2 Image Feature Extraction Methods 25 4.2.1 The Sun Block Cropping 26 4.2.2 Regional Cloud Feature Extraction 28 4.2.2.1 The improved RB method 28 4.2.2.2 The sun area elimination 30 4.2.2.3 The distance consideration 30 4.2.2.4 The regional weights calculation 31 4.2.3 Global Cloud Feature Extraction 33 4.3 Predicted Sun Block Image Production 36 4.3.1 The Average Moving Vector Calculation 37 4.3.2 The Predicted Sun Block Image Production 39 Chapter 5 Experimental Results 45 5.1 Database 45 5.2 Performance Indices 46 5.3 Solar Irradiance Estimation 47 5.4 Solar Power Estimation 69 5.5 Solar Irradiance Prediction 72 Chapter 6 Conclusion and Future Works 83 6.1 Conclusion 83 6.2 Future Works 84 References 86 Autobiography 89 Academic Achievement 90

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