臺灣博碩士論文加值系統

大學院校之電費掌控，係屬物業管理重要工作之一。舉凡編列預算及節能成效，均須分析及預測用電紀錄，以瞭解用電量的變化情形。現有的用電量預測相關文獻多著墨商業類及住宅類建物，對於他類建築少有文獻可茲參考依循。傳統預測方法大多需要大量的歷史資料做為建置預測模型的基礎，且必須符合統計上的相關檢定，因此在應用上有其限制。
有鑑於學校用電具有週期性的特性，本研究以雲科大圖書館為對象，蒐集九十三至九十五年間圖書館每月用電歷史資料，分別利用時間序列迴歸模型結合傅立葉級數模型及用電量平均法預測九十六年圖書館之每月用電量，再以實際用電量比較確認何者之預測精度較佳。
研究結果顯示，在資料有限的情境下預測圖書館用電量，以誤差平方和(SSE)為模型精準之判斷依據，發現傅立葉級數的預測準確度(SSE=4.16×10^9)優於用電量平均法(SSE=5.15×10^9)。而利用三種不同數據型態進行傅立葉級數預測，發現以九十三至九十五年每月用電歷史資料之預測值優於九十五年、九十四至九十五年兩種數據型態。造成此結果之原因，可能係利用歷史資料越長、時間越長，越能顯示用電量變動之趨勢。此外，本研究亦利用所預測出之用電最佳預測值，計算流動電費並與實際流動電費比較。結果顯示其誤差值為3.98%、精度值高達96.02%，此一結果可供校方規劃未來電力預算之參考。
本研究以傅立葉級數採用少量資料預測用電量，研究架構及結果仍處初步階段，對於隨機因素(Stochastic Factors)造成的變動及歷史資料不足之限制，仍待後續研究進一步探討。後續研究亦可設計一套操作平台（如VB），俾利使用者方便且快速執行相關預測。

Electricity expense is one of the fundamental tasks of property management. Activities, such as budget making and energy conservation programs, depend on the analysis on and estimate of electricity consumption. Most of the reference regarding the estimate of electricity consumption focuses more on the commercial and residential buildings than the other types of buildings. The traditional estimate method requires a large amount of historical data to construct the estimate model; also, it has to meet the correlation examine statistically. Therefore, the traditional estimate method has its limits.
In consideration of the periodicity of electricity consumption in educational institutions, this research focuses on the library of National Yunlin University of Science and Technology. Based on the collected historical records of monthly electricity consumption from 2004 to 2006, the monthly electricity consumption of 2007 is estimated by using the time series regression model in combination with the Fourier series model and the electricity consumption average method respectively. By comparing the estimate results to the real electricity consumption, the precision of different models can be identified.
This research uses sum of squares error (SSE) to determine the precision of models. The result indicates that Fourier series model (SSE=4.16×10^9) reaches a more precise estimate than the electricity consumption average method (SSE=5.15×10^9). Three data types (monthly electricity consumption during 2004-2006, 2006 and 2005-2006) are used to conduct the Fourier series estimate. The estimate based on the monthly electricity consumption during 2004-2006 is superior to the estimates based on the other two data types, which might resulted from a more accurate variation trend of electricity consumption reflected by historic data of a longer period. Also, the estimated optimized electricity consumption is used to calculate the floating electricity expense, which, compared with the real floating electricity expense, shows the error is 3.98%. The result could be a helpful reference for NYUST in budget making.
This research is based on the Fourier series model and a relatively small amount of data to estimate the electricity consumption. Further study is required to deal with the variation caused by the stochastic factors and limits caused by insufficient historical data. Also, the development of operating platforms, such as VB, is suggested to facilitate related estimate.

摘要 i
英文摘要 ii
誌謝 iv
目錄 v
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研究目的 5
1.3研究對象與範圍 5
1.4研究方法 6
1.5研究流程 7
1.6論文架構 9
第二章 文獻回顧 10
2.1預測定義與方法之整理 10
2.1.1預測之定義 10
2.1.2預測方法之分類 11
2.1.3預測方法簡介 12
2.2相關用電量預測文獻回顧 15
2.3傅立葉級數相關應用文獻 17
2.4結語 19
第三章 研究方法 20
3.1迴歸分析 20
3.1.1基本概念 20
3.1.2簡單迴歸 20
3.1.3迴歸的原理與特性 20
3.2傅立葉級數 23
3.2.1一般概念與定義 23
3.2.2傅立葉係數 25
3.2.3週期為任意值之函數 29
3.2.4其他形式之傅立葉級數 31
3.3結語 33
第四章 模型建構 34
4.1案例介紹 34
4.2雲科大圖書館用電情況 39
4.3預測模型之建立 40
4.4結語 47
第五章 預測結果 49
5.1迴歸模型結果 49
5.2傅立葉級數模型結果 50
5.3結果比較 60
5.4用電量計價 61
5.5結語 63
第六章 結論與建議 65
6.1結論 65
6.2建議 66
參考文獻 67

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