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研究生:林景徽
研究生(外文):Jing-Hui Lin
論文名稱:解制環境之負載預測與電力價格預測
論文名稱(外文):Forecasting of Load and electricity Price in Deregulated Markets
指導教授:梁瑞勳梁瑞勳引用關係
指導教授(外文):Ruey-Hsun Liang
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:電機工程系碩士班
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:2004
畢業學年度:92
語文別:中文
論文頁數:108
中文關鍵詞:負載預測電力價格解制電力價格預測迴歸分析模糊系統法
外文關鍵詞:fuzzy systemregression analysiselectricity pricederegulatedload forecastingprice forecasting
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在解制的電業環境中,發電業者與消費者需要精確的電價預測值來計劃競標策略,使每一方皆能得到最大利益,因此如何實現電價預測是值得關切的。台灣電力事業正朝著自由化市場邁進,除了精準的負載預測外,實現電價預測方能為解制環境提供不可或缺的參考數據。
由於負載對電力價格有很大的影響力,初步必須求得負載預測值,再利用此預測量更精確地得到電價預測值。本論文分別以複線性迴歸分析與模糊系統法為基礎,作預測的工作,迴歸分析具有原理簡單、模型建立容易等優點;模糊系統法可減少複雜的數學模型,並利用經驗法則處理許多問題,本論文將分別探討兩種方法的優劣性。最後更提出改良型之預測模式,修正預測所造成的誤差,使結果更能勝任。
為了證明本論文所提架構的優越性,使用台灣電力公司與中央氣象局的實際負載與溫度資料來作負載預測;因為台灣尚未解制,所以缺乏市場電價之歷史資料,因此利用台灣電力公司之實際發電資料模擬出之逐時電力價格作為歷史觀察值,一旦市場解制後,即可依照本文提出的架構預測市場電價值。測試實例包含夏季工作日與週末日之負載、電價預測,所得結果令人滿意。
In the deregulated electricity environment, power producers and consumers need accurate price forecasting to plan bidding strategies in order to maximum their benefits.
Moreover, load effect electricity price greatly, so how to achieve load forecasting is also essential. This thesis presents multiple regression analysis and fuzzy system method to obtain the estimated load first, because the modeling of regression analysis is simple and useful, and the fuzzy system method use some experience rules can solve many problems. Sequentially, inputting the estimated load to the price-regression models or price-fuzzy inference mechanism can derive more exact expected price.
The introduced data of this structure contain demand data of Taiwan Power Company (Taipower) and temperature data of Central Weather Bureau. However, electrical power industry in Taiwan is not yet deregulated, therefore, to simulate price data, the actual generation data of Taipower electricity is used as historical and observed values. As long as market is deregulated, using these approaches may forecast market price in the future effectively. This thesis presents regression analysis and fuzzy system for the forecasting load and price, and the result is quite accurate.
中文摘要 -------------------------------------------------------------------------- i
英文摘要 -------------------------------------------------------------------------- ii
誌謝 -------------------------------------------------------------------------- iii
目錄 -------------------------------------------------------------------------- iv
表目錄 -------------------------------------------------------------------------- vii
圖目錄 -------------------------------------------------------------------------- ix
第一章 緒論-------------------------------------------------------------------- 1
1.1 研究背景與動機----------------------------------------------------- 1
1.2 研究方法-------------------------------------------------------------- 2
1.3 論文大綱-------------------------------------------------------------- 3
第二章 解制環境之負載與電力價格預測-------------------------------- 5
2.1 前言-------------------------------------------------------------------- 5
2.2 文獻回顧-------------------------------------------------------------- 6
2.3 本論文之預測架構-------------------------------------------------- 8
2.4 採用的資料----------------------------------------------------------- 10
2.4.1 短期負載預測所採用之歷史資料-------------------------------- 10
2.4.2 短期電價預測所採用之歷史資料-------------------------------- 10
2.5 本章結論-------------------------------------------------------------- 11
第三章 利用複線性迴歸分析實現負載預測與電價預測-------------- 12
3.1 前言-------------------------------------------------------------------- 12
3.2 複線性迴歸分析之數學模式-------------------------------------- 12
3.2.1 相關係數之分析----------------------------------------------------- 13
3.2.2 迴歸係數之求得----------------------------------------------------- 14
3.2.3 數學模型之建立----------------------------------------------------- 16
3.3 負載迴歸模型與電價迴歸模型之建立-------------------------- 16
3.3.1 負載模型之變數選取與分析-------------------------------------- 17
3.3.2 電價模型之變數選取與分析-------------------------------------- 22
3.3.3 預測流程與架構----------------------------------------------------- 25
3.4 實例測試與分析----------------------------------------------------- 26
3.4.1 短期負載預測-------------------------------------------------------- 27
3.4.2 短期電價預測-------------------------------------------------------- 32
3.5 本章結論-------------------------------------------------------------- 37
第四章 利用模糊系統法實現負載預測與電價預測-------------------- 38
4.1 前言-------------------------------------------------------------------- 39
4.2 模糊推理機之建立-------------------------------------------------- 39
4.2.1 模糊化----------------------------------------------------------------- 40
4.2.2 模糊知識庫----------------------------------------------------------- 40
4.2.3 決策邏輯-------------------------------------------------------------- 41
4.2.4 解模糊化-------------------------------------------------------------- 42
4.3 負載預測與電價預測模糊推理機之建立----------------------- 42
4.3.1 負載預測之模糊推理機的變數選取與分析-------------------- 43
4.3.2 電價預測之模糊推理機的變數選取與分析-------------------- 45
4.3.3 建立模糊知識庫----------------------------------------------------- 47
4.3.4 輸入新數值至推理機進行負載與電價預測-------------------- 49
4.4 實例測試與分析----------------------------------------------------- 49
4.4.1 短期負載預測-------------------------------------------------------- 50
4.4.2 短期電價預測-------------------------------------------------------- 55
4.5 本章結論-------------------------------------------------------------- 60
第五章 利用改良型之預測模式實現負載預測與電價預測----------- 61
5.1 前言-------------------------------------------------------------------- 61
5.2 改良型之預測模式-------------------------------------------------- 62
5.2.1 初步預測模型-------------------------------------------------------- 63
5.2.2 修正模型-------------------------------------------------------------- 63
5.3 建立負載預測與電價預測之修正模型-------------------------- 64
5.3.1 負載預測之修正模型的變數選取與分析----------------------- 64
5.3.2 電價預測之修正模型的變數選取與分析----------------------- 68
5.3.3 預測流程與架構----------------------------------------------------- 71
5.4 實例測試與分析----------------------------------------------------- 72
5.4.1 短期負載預測-------------------------------------------------------- 73
5.4.2 短期電價預測-------------------------------------------------------- 78
5.5 本章結論-------------------------------------------------------------- 85
第六章 結論與未來研究方向----------------------------------------------- 86
6.1 結論-------------------------------------------------------------------- 86
6.2 未來研究方向-------------------------------------------------------- 88
參考文獻 -------------------------------------------------------------------------- 89
作者簡介 -------------------------------------------------------------------------- 93
參考文獻

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