臺灣博碩士論文加值系統

　　本研究主要目的在應用高階統計方法（Higher Order Statistics）
於電力系統短期負載預測，以期改善傳統ARMA（Auto Regression
Moving Average）負載預測模式之準確性。由於傳統ARMA負載預測模式係
假設負載或影響負載之各項氣候因素時間序列為高斯過程（Gaussian
Process），因此對於模式階數架構選定，參數估計及模式驗證係利用資
料本身自我（Auto）或部份自我（Partial Auto ）相關函數（Correl
-ation Function）。因此，基本上傳統方法應用範圍應侷限於線性，最
小相位（Minimum Phase）及高斯系統上。事實上，短期負載預測並不完
全符合此一要求，因此傳統方法在假設上實有重新加以探討必要。本論文
除考慮時間序列資料一、二階統計值外，並加入高階統計值，即累積值（
Cumulants），期充分擷取資料本身所含資訊（Maximum Infor- mation
Extraction），以期改進短期負載預測準確性。由於高階統計方法適合於
解決非線性、非高斯、非最小相位及有色雜訊系統的問題，故本文內所探
討之系統負載除全系統外，包括區域性輸配電系統負載如變電所主變壓器
，饋線等不同類型負載。本論文先建立高階統計模式及參數辨證與估計的
方法，初步驗證所建立的模式。同時著重負載時間序列本身高階統計預測
方法的建立，即單變數靜態之ARMA模式，暫不加入氣候因素的影響，以簡
化問題初期之研究。最後將所發展方法應用於真實台電全系統及區域性負
載以驗證其實用性，再與傳統二階方法比較。本研究內並發展一套方法用
以分析負載型態的高斯特性，同時探討負載高斯特性對負載預測方法準確
度的關係。

A higher order statistics approach is attempted to
improve the accuracy of conventional ARMA (AutoRegression
Moving Average) model in short term power system load
forecasting. Due to the assumption of Gaussian process on the
time series of load, the model structure determination, the
parameter estimation and the model validation of conventional
approach are all based on the calculation of ACF (Auto
Correlation Function) and PACF (Partial Auto Correlation
Function), as in the well known Box and Jenkins method. In a
strict sense, the conventional approach should be confined to
the system of linearity, minimum phase and Gaussianity.
However, the problem of short term load forecasting does not
adhere to these limitations. The assumptions of the
conventional method on the short term load forecasting need to
be further investigated. This thesis employs higher order
statistics (i.e., cumulants) of the load time series
to extract maximum information contained in the data, and
thus to improve the accuracy of the short term load
forecasting. Since the higher order statistics approach can be
applicable to the problems of nonlinearity, non-Gaussianity,
nonminimum phase and colored noise, the power system load
studied in this thesis comprises different statistic patterns
of the whole system and the local load demands. The local load
demand includes those of the main transformer and the feeder
at a substation. A number of methods have been developed
to obtain the structure and parameters of the higher order
statistics model. Besides, the Monte Carlo simulation is
used to validate the method and computer programs developed.