HIGHER ORDER STATISTICS APPROACH TO POWER SYSTEM SHORT TERM LOAD FORECASTING

• Main Authors: Huang, Ming-Shung, 黃明舜
• Other Authors: C.L.Huang, H.Z.Yang, Y.T.Hsiao
• Format: Others
• Language: zh-TW
• Published: 1993
• Online Access: http://ndltd.ncl.edu.tw/handle/41739120232700589588

碩士 === 國立成功大學 === 電機工程研究所 === 81 === 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.