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研究生:林逸松
研究生(外文):Yi-Song Lin
論文名稱:以支援向量回歸預測太陽能系統短期輸出
論文名稱(外文):Forecasting Short-Term Power Output of Photovoltaic Systems Based on Support Vector Regression
指導教授:劉志文劉志文引用關係
指導教授(外文):Chih-Wen Liu
口試委員:林子喬蘇恆毅
口試委員(外文):Tzu-Chiao LinHeng-Yi Su
口試日期:2019-06-28
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電機工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:中文
論文頁數:70
中文關鍵詞:太陽能預測支援向量回歸k-平均演算法
DOI:10.6342/NTU201901628
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太陽能是未來主要的能源供給,但是其供電受天氣影響並不穩定。預測發電量是可行解決之道,配上適當的電力調度可使電力供給穩定,但是如何使其預測準確又是問題。本論文嘗試以支援向量回歸來進行預測,因為支援向回歸原理簡單,發展完整,容易改良預測結果。以台電跟中央氣象局過往資料進行預測,最後我們結合k-平均演算法和支援向量回歸可使預測平均絕對值誤差縮小為總裝置容量的7 % ,並與決策樹、類神經網路、k-鄰近法進行比較。之後進行延伸,嘗試預測台電即時機組發電量,也證實此方法實作是可行的。之後以插值的方式加上天氣預報來預測近日發電模型,不過其準確度受限於天氣預報。
Photovolatic will be the major power supply in the future, but it is not stable due to the weather condtion. Forecasting power output of PV system and optimal power dispatach can solve this problem, but how to accurate prediction? This thesis tries to predition based on support vector regression(SVR) and improve this method. The power data is collected from Taiwan Power Company and the weather data is collected from Taiwan Central Weather Bureau(TCWB). We use those historical data to forecast the PV output. Finally, we propose algorithm that is combined by K-Means Algorithm and SVR.It’s mean relative error is reduced to 7 %. This algorithm has better prediction accuracy than regression tree, K nearest neighbors regression and neural network. We extend this method to the online forecast. It still works, but needs to improve. Use the interpolation and weather forecast to predict the receant PV power output. Beacause of the inaccurate weather forecast, but its accuracy is limited by weather forecast.
致謝 III
摘要 IV
ABSTRACT V
目錄 VI
圖目錄 VIII
表目錄 XI
第一章 緒論 1
1-1 研究背景 1
1-2 研究目標 3
1-3 文獻回顧討論 3
1-4 章節摘要 3
第二章 支援向量回歸 5
2-1 前言 5
2-2 支援向量機 5
2-2-1 線性支援向量機 5
2-2-2 軟限制支援向量機 11
2-2-3 非線性支援向量機 13
2-3 支援向量回歸 17
2-3-1 Tube Regression 17
2-3-2 二次規劃 20
第三章 支援向量回歸預測 27
3-1 太陽能資料 27
3-2 時間序列 29
3-3 天氣資料 39
3-4 依天氣分類預測 42
3-4-1 k-平均演算法(K-Means Algorithm) 44
第四章 其他預測方法 48
4-1 回歸樹 48
4-2 K-鄰近法 (K Nearest Neighbors) Regression 51
4-3 Neural Network 53
4-3-1 Feedforward Neural Network 54
4-3-2 Recurrent Neural Network 54
4-3-3 Deep Recurrent Neural Network(DRNN) 56
第五章 更短期即時預測 58
5-1 前言 58
5-2 核三生水池光電更短期預測 60
5-3 天氣預報加入預測 63
第六章 結論與未來研究方向 67
6-1 結論 67
6-2 未來研究方向 67
參考文獻 68
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