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研究生:廖大榮
研究生(外文):Da-Rung Liao
論文名稱:以ARMA模型預測太陽能電廠發電量之研究
論文名稱(外文):Using an ARMA Model to Predict Electrical Production of a Solar Power Plant
指導教授:王順成白子易白子易引用關係
指導教授(外文):Shun-Cheng WangTzu-Yi Pai
學位類別:碩士
校院名稱:朝陽科技大學
系所名稱:環境工程與管理系碩士班
學門:工程學門
學類:環境工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:95
中文關鍵詞:發電量ARMA模型新伯公倉庫斜屋頂太陽能電廠
外文關鍵詞:ARMA modelXinbogong Warehouse Slant-roof Solar Power PlantElectrical Production
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摘要
本研究應用ARMA 階數模型預測「台電供電處-新伯公倉庫斜屋頂太陽能電廠」發電量。一般利用AR或MA配適模型時,有時需要較多參數才能配適出合適的模型,為了克服此點困難,利用ARMA模型或許可以精簡配適模型之參數。研究結果顯示,預測時的RMSE介於9.2449至12.8915之間。對於R值而言,預測時的R值介於0.39至0.69之間。以ARMA (2, ) 對新伯公倉庫斜屋頂太陽能電廠發電量進行預測時,以ARMA (2,9) 模型之結果較佳,在預測發電量時,RMSE為10.0013,R值為0.63。
Abstract
This study used an ARMA stage model to predict electrical production of “Taipower Power Station of Xinbogong Warehouse slant roof solar power plant”. Utilizing either AR or MA to fit our models would sometimes require more variables. In order to overcome this limitation, using an ARMA model may simplify and reduce the variables required to fit our model. The results showed that predicted RMSE was between 9.2449 and 12.8915, with an R value between 0.39 and 0.69 When predicting the electrical production of Xinbogong Warehouse Slant-roof Solar Power Plant, this study obtained better results using an ARMA (2,9) model, with an RMSE was 10.0013 and R value was 0.63.
目 錄
摘要 I
Abstract II
誌謝 III
目 錄 IV
表目錄 VII
圖目錄 VIII
第一章 前言 1
1.1 研究緣起 1
1.2 研究目的 4
第二章 文獻回顧 6
2.1 我國有關太陽光電的法規背景 6
2.1.1 再生能源發展條例通過 6
2.1.2電業法 7
2.1.3 內政部營建署函建築物雜項執照 8
2.1.4 台電再生能源電源購電作業要點 8
2.1.5 台灣電力公司再生能源發電系統併聯技術要點 9
2.2 太陽能發電與環境保護回顧 11
2.3 ARMA理論之應用 19
第三章 研究方法 23
3.1自回歸模型 25
3.1.1自回歸模型 25
3.1.2 AR(p)的自相關函數 28
3.1.3 序列的自相關係數的作用 29
3.1.4 模型的平穩解 29
3.2移動平均模型 32
3.2.1 移動平均模型描述 32
3.2.2 的自相關函數 34
3.2.3 的自相關函數 34
3.2.4 序列的自協方差函數 36
3.3 自回歸移動平均模型 37
3.3.1 序列 37
3.3.2 因果 序列 41
3.3.3  的自協方差函數和自相關函數 42
3.3.4 ARMA(p, q)序列的自協方差函數 43
第四章 結果與討論 45
4.1 發電量預測結果 45
4.2 發電量預測討論 75
第五章 結論與建議 77
5.1 結論 77
5.2 建議 77
參考文獻 78



表目錄
表4.1 ARMA(2, )模型各項係數整理 54
表4.1 ARMA(2, )模型各項係數整理 55
表4.2 ARMA(2, )模型預測太陽能電廠發電量之模擬效能 56
表4.3 ARMA(3, )模型各項係數整理表 61
表4.3 ARMA(3, )模型各項係數整理 62
表4.4 ARMA (3, ) 型預測太陽能電廠發電量之模擬效能 63
表4.5 ARMA(4, )模型各項係數整理 68
表4.5 ARMA(4, )模型各項係數整理 69
表4.6 ARMA (4, )模型預測太陽能電廠發電量之模擬效能 70
表4. 7ARMA(2, ) (3, ) (4, )模型預測太陽能電廠發電量之模擬效能 76

圖目錄
圖1-1研究架構圖 5
圖2-1國內太陽能光電系統設置推動方案 14
圖2-2太陽能光電系統設備組成 16
圖2-3太陽能發電成本趨勢 18
圖3.1 研究地點 24
圖4.1 ARMA (2, 2) 預測太陽能電廠發電量擬合程度 57
圖4.2 ARMA (2, 3) 預測太陽能電廠發電量擬合程度 57
圖4.3 ARMA (2, 4) 預測太陽能電廠發電量擬合程度 58
圖4.4 ARMA (2, 5) 預測太陽能電廠發電量擬合程度 58
圖4.5 ARMA (2, 6) 預測太陽能電廠發電量擬合程度 59
圖4.6 ARMA (2, 7) 預測太陽能電廠發電量擬合程度 59
圖4.7 ARMA (2, 8) 預測太陽能電廠發電量擬合程度 60
圖4.8 ARMA (2, 9) 預測太陽能電廠發電量擬合程度 60
圖4.9 ARMA (3,2) 預測太陽能電廠發電量擬合程度 64
圖4.10 ARMA (3,3) 預測太陽能電廠發電量擬合程度 64
圖4.11 ARMA (3,4) 預測太陽能電廠發電量擬合程度 65
圖4.12 ARMA (3,5) 預測太陽能電廠發電量擬合程度 65
圖4.13 ARMA (3,6) 預測太陽能電廠發電量擬合程度 66
圖4.14 ARMA (3,7) 預測太陽能電廠發電量擬合程度 66
圖4.15 ARMA (3,8) 預測太陽能電廠發電量擬合程度 67
圖4.16 ARMA (3,9) 預測太陽能電廠發電量擬合程度 67
圖4.17 ARMA (4,2) 預測太陽能電廠發電量擬合程度 71
圖4.18 ARMA (4,3) 預測太陽能電廠發電量擬合程度 71
圖4.19 ARMA (4,4) 預測太陽能電廠發電量擬合程度 72
圖4.20 ARMA (4,5) 預測太陽能電廠發電量擬合程度 72
圖4.21 ARMA (4,6) 預測太陽能電廠發電量擬合程度 73
圖4.22 ARMA (4,7) 預測太陽能電廠發電量擬合程度 73
圖4.23 ARMA (4,8) 預測太陽能電廠發電量擬合程度 74
圖4.24 ARMA (4,9) 預測太陽能電廠發電量擬合程度 74
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