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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 江昭皚(Joe-Air Jiang) | |
| dc.contributor.author | Ling-Chieh Tai | en |
| dc.contributor.author | 戴令絜 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:19:21Z | - |
| dc.date.available | 2021-11-08 | |
| dc.date.available | 2022-11-24T03:19:21Z | - |
| dc.date.copyright | 2021-11-08 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-04 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80858 | - |
| dc.description.abstract | "隨著環保意識增長及尋求能源的永續發展，再生能源的發展成為各國重點施政項目，根據國際能源署(International Energy Agency, IEA)統計，全球再生能源發電量年年上升，預估在 2019 年至 2024 年再生能源發電容量能夠成長 50%，增加1200 GW，其中太陽能光電系統成長最鉅，發電容量增長 60%，增加 500GW。台灣的再生能源發電比例約占總發電量的 15%，太陽能則為第二大再生能源，佔再生能源總發電量的 28.2%，且呈現逐年上升的趨勢。太陽能發電會因天氣因素而有劇烈波動，可能產生供電間歇性及隨機性等問題，而影響電力系統的安全及穩定。本研究提出一太陽能光電系統發電量預測方法，以衛星雲圖資料及環境參數作為輸入參數，輸入雲圖至卷積神經網路提取雲圖特徵，建立可適用於廣域發電預測的模型。在太陽能光電系統預測中，長時間預測容易忽略掉太陽輻照度的變化，因此本研究採用短時間間隔預測太陽輻照度，並分別討論晴天及陰天的情況。並依照輻照度預測結果，與歷史資料做迴歸曲線分析，找出迴歸方程式與相關性分析，藉此預測未來太陽能光電系統之發電量。 " | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:19:21Z (GMT). No. of bitstreams: 1 U0001-2709202121224900.pdf: 4993958 bytes, checksum: 2dfecbe35fd286ce3d14e546045d9758 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 誌謝 ........................................... i 摘要 ........................................... iv Abstract ....................................... v Table of Contents.............................. vii List of Figures ................................ x List of Tables ................................. xiii Chapter 1 Introduction ......................... 1 1.1 Background ................................. 1 1.2 Motivation and propose...................... 5 1.3 Thesis organization ........................ 5 Chapter 2 Literature Review .................... 7 2.1 Photoelectric system power generation efficiency..................... 7 2.2 Solar Irradiation Prediction ........................................ 12 2.2.1 Artificial Neural Network Models................................... 16 2.2.2 Numerical weather prediction (NWP) model........................... 17 2.2.3 Genetic algorithm (GA)............................................. 18 2.2.4 Autoregressive Moving Average (ARMA) model......................... 20 2.2.5 Support vector machine (SVM) model................................. 20 2.3 Satellite Images..................................................... 21 2.3.1 Visible light cloud image and irradiance .......................... 22 2.4 Convolutional Neural Network ........................................ 23 2.4.1 Convolution Layer.................................................. 24 2.4.2 Pooling Layer ..................................................... 25 2.4.3 Fully Connected Layer ............................................. 26 2.4.4 Pre-trained Model.................................................. 26 Chapter 3 Materials and Methods ......................................... 29 3.1 Framework of the proposed method .................................... 29 3.1.1 The architecture of the solar irradiance prediction system......... 29 3.2 Experimental Equipment............................................... 32 3.3 Satellite Cloud Images and Feature Extraction........................ 36 3.4 Model Implementation ................................................ 37 3.4.1 Criteria for performance evaluation ............................... 37 Chapter 4 Results and Discussion ........................................ 40 4.1 Convolutional Neural Network for Predicting Irradiance............... 40 4.1.1 Data sets.......................................................... 40 4.1.2 Finding the best combination of the optimizer and epochs........... 42 4.1.3 Finding the best combination of neurons ........................... 46 4.1.4 The performance of the training model ............................. 47 4.2 Wide-area prediction predictive method .............................. 56 4.2.1 Multi-objective optimization ...................................... 58 4.2.2 Wide-area prediction results....................................... 60 Chapter 5 Conclusion..................................................... 68 References .............................................................. 72 | |
| dc.language.iso | en | |
| dc.subject | 太陽能光電系統 | zh_TW |
| dc.subject | 衛星雲圖 | zh_TW |
| dc.subject | 發電量預測 | zh_TW |
| dc.subject | Satellite Cloud image | en |
| dc.subject | Power Generation | en |
| dc.subject | Photovoltaic System | en |
| dc.title | 應用卷積神經網路於廣域太陽光電系統之發電預測與分析 | zh_TW |
| dc.title | Forecast and Analysis of Power Generation for Wide-Area Photovoltaic Systems Based on Convolutional Neural Network | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 蕭瑛東(Hsin-Tsai Liu),王永鐘(Chih-Yang Tseng),周呈霙,王人正 | |
| dc.subject.keyword | 太陽能光電系統,衛星雲圖,發電量預測, | zh_TW |
| dc.subject.keyword | Photovoltaic System,Satellite Cloud image,Power Generation, | en |
| dc.relation.page | 77 | |
| dc.identifier.doi | 10.6342/NTU202103416 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-10-06 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 生物機電工程學系 | zh_TW |
| 顯示於系所單位： | 生物機電工程學系 | |
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