臺灣博碩士論文加值系統

由於太陽光電並不會穩定的輸出電力，其原因是太陽光在照射到地球表面的過程中容易受到空氣中的物質所影響，例如雲層、雜質…等。當太陽光照射至太陽光電模組的過程中受到雲層等物質的遮蔽，太陽光電模組會立即降低發電量；太陽光電模組亦會因太陽能電池的材質、溫度、架設的地點以及面向方位而影響發電的效率。
本論文主要目的在於應用倒傳遞類神經網路技術於預測領前1至24小時之太陽能發電量，並分析於台中光電廠之發電預測效果。利用8種不同的輸入組合，架構倒傳遞類神經網路並比較各方法預測效果之優劣，最後選擇其中一種方法進行太陽能發電預測。根據預測結果顯示，加入未來因子之預測方法具有較好的預測結果。

Because solar irradiance is susceptible to clouds and substances in the air, the solar photovoltaic cannot produce stable power output. The power output of a photovoltaic module is influenced immediately when the module is sheltered from the clouds. Besides, the material of solar cell, air temperature, module’s position and orientation also affect the power output of the photovoltaic module.
The main goal of the thesis is to develop the solar power forecasting with 24 hours ahead by applying back-propagation neural network technology. Some different combination inputs of the back-propagation neural network are proposed and their forecasting performances are evaluated. Moreover, comparison results in Taichung solar farm are given. As a result, the better performance is achieved by the inputs with combination of future factors.

摘要 i
Abstract ii
謝誌 iii
目錄 iv
表目錄 vi
圖目錄 vii
第壹章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 5
1.3 研究架構 7
第貳章 文獻探討與回顧 9
2.1 日射量 9
2.2 太陽能發電系統 11
2.2.1 太陽能發電的原理 12
2.2.2 台中電廠太陽光電站 14
2.3 類神經網路 16
2.3.1 倒傳遞類神經網路 19
第參章 研究方法 27
3.1 資料收集 27
3.1.1 太陽位置 28
3.1.2 日射量理論計算值 30
3.2 倒傳遞類神經網路之建立 31
3.3 網路效能評估指標 34
第肆章 實驗結果與討論 37
4.1 相關性分析 37
4.2 類神經網路預測 38
4.2.1 全天歷史日射量為基礎 40
4.2.2 白天歷史日射量為基礎 47
4.2.3 白天歷史日射量與現在溫度為基礎 54
4.2.4 白天歷史日射量與高度角為基礎 61
4.2.5 白天歷史日射量與現在溫度、高度角為基礎 68
4.2.6 日射量差值與高度角為基礎 75
4.2.7 日射量差值與現在溫度、高度角為基礎 82
4.2.8 歷史日射量與未來日射量為基礎 89
4.2.9 小結 96
第伍章 結論與未來展望 117
5.1 結論 117
5.2 未來展望 118
參考文獻 119

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