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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/60096

    Title: 基於EEMD之倒傳遞類神經網路方法對用電量及黃金價格之預測
    Forecasting electricity consumption as well as gold price by using an EEMD-based Back-propagation Neural Network Learning Paradigm
    Authors: 蔡羽青
    Tsai, Yu Ching
    Contributors: 蕭又新
    Shiau, Yuo Hsien
    Tsai, Yu Ching
    Keywords: 總體經驗模態分解法
    Ensemble Empirical Mode Decomposition
    Back-propagation Neural Network
    electricity consumption forecasting
    gold price forecasting
    very-short term load forecasting
    Date: 2011
    Issue Date: 2013-09-04 15:28:15 (UTC+8)
    Abstract: 本研究主要應用基於總體經驗模態分解法(EEMD)之倒傳遞類神經網路(BPNN)預測兩種不同的非線性時間序列數據,包括政大逐時用電量以及逐日歷史黃金價格。透過EEMD,這兩種資料會分別被拆解為數條具有不同物理意義的本徵模態函數(IMF),而這讓我們可以將這些IMF視為各種影響資料的重要因子,並且可將拆解過後的IMF放入倒傳遞類神經網路中做訓練。


    In this paper, we applied the Ensemble Empirical Mode Decomposition (EEMD) based Back-propagation Neural Network (BPNN) learning paradigm to two different topics for forecasting: the hourly electricity consumption in NCCU and the historical daily gold price. The two data series are both non-linear and non-stationary. By applying EEMD, they were decomposed into a finite, small number of meaningful Intrinsic Mode Functions (IMFs). Depending on the physical meaning of IMFs, they can be regarded as important variables which are input into BPNN for training.

    We also use moving-window method in the prediction process. In addition, cubic spline interpolation as well as extrapolation as our strategy is applied to electricity consumption forecasting, these two methods are used for smoothing the data and finding local trend to improve accuracy of results.

    The prediction results using our methods and strategy resulted in good accuracy. However, for further accuracy, we used the ensemble average method, and compared the results with the data produced without applying the ensemble average method. By using the ensemble average, the outcome was more precise with a smaller error, it results from the procedure of finding minimum error function in the BPNN training.
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    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0098755011
    Data Type: thesis
    Appears in Collections:[應用物理研究所 ] 學位論文

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