English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 93861/124308 (76%)
Visitors : 28943217      Online Users : 511
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    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
    蔡羽青
    Tsai, Yu Ching
    Keywords: 總體經驗模態分解法
    倒傳遞類神經網路
    用電量預測
    黃金價格預測
    超短時間負荷預測
    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。

    另外在本文中,我們也採用移動視窗法作為預測過程中的策略,另外也應用內插法和外插法於逐時用電量的預測。內插法主要是用於補點以及讓我們的數據變平滑,外插法則可以在某個範圍內準確預測後續的趨勢,此兩種方法皆對提升預測準確度占有重要的影響。

    利用本文的方法,可在預測的結果上得到不錯的準確性,但為了進一步提升精確度,我們利用多次預測的結果加總平均,然後和只做一次預測的結果比較,結果發現多次加總平均後的精確度的確大幅提升,這是因為倒傳遞類神經網路訓練過程中其目標為尋找最小誤差函數的關係所致。
    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.
    Reference: Ajay Shekhar Pandey, Devender Singh, and Sunil Kumar Sinha, 2010. Intelligent Hybrid Wavelet Models for Short-Term Load Forecasting. IEEE transactions on power systems, VOL. 25, NO. 3, August 2010.

    Akgiray, V., G.G. Booth, J.J. Hatem, and C. Mustafa, 1991. Conditional Dependence in Precious Metal Prices. The Financial Review, 26, 367-386.

    Chen, M.-C., Wei, Y, 2010. Exploring time variants for short-term passenger flow. J. Transp. Geogr. doi:10.1016/j.jtrangeo.2010.04.003

    Cummings, D.A.T., Irizarry, R.A., Huang, N.E., Endy, T.P.,
    Nisalak, A., Ungchusak, K., Burke, D.S., 2004. Travelling waves in the occurrence of dengue haemorrhagic fever in Thailand. Nature 427 (6972), 344–347.

    En Tzu Li, 2011. TAIEX Option Trading by using EEMD-based Neural Network Learning Paradigm. Master Thesis of Graduate Institute of Applied Physics, College of Science NCCU.

    FENG Ping, DING Zhi-hong, HAN Rui-guang, ZHANG Jian-wei. 2009. Precipitation-runo forecasting ANN model based on EMD. Systems Engineering-Theory & Practice, Vol.29, No.1, Jan., 2009.

    G.A. Adepoju, M.Sc., S.O.A. Ogunjuyigbe, M.Sc., and K.O. Alawode, B.Tech. Application of Neural Network to Load Forecasting in Nigerian Electrical Power System. The Pacific Journal of Science and Technology Volume 8. Number 1. May 2007 (Spring).

    Hwang, P.A., Huang, N.E., Wang, D.W., 2003. A note on analyzing nonlinear and non-stationary ocean wave data. Applied Ocean Research 25 (4), 187–193.

    Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.-C., Tung, C.C., Liu, H.H., 1998. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London A 454 (1971), 903–995.

    Huang, N.E., Wu, M.L., Qu, W.D., Long, S.R., Shen, S.S.P., 2003b. Applications of Hilbert–Huang transform to nonstationary financial time series analysis. Applied Stochastic Models in Business and Industry 19, 245–268.

    Hari Seetha and R. Saravanan, 2007. Short Term Electric Load Prediction Using Fuzzy BP. Journal of Computing and Information Technology - CIT 15, 2007, 3, 267–282.

    Hong Ying Yang, Hao Ye, Guizeng Wang, Junaid Khan, Tongfu Hu, 2005. Fuzzy neural very-short-term load forecasting based on chaotic dynamics reconstruction. Chaos, Solitons & Fractals Volume 29, Issue 2, July 2006, Pages 462-469

    Hamid S. A. and Iqbal Z., Using neural networks for
    forecasting volatility of S&P 500 Index futures prices, Journal of Business Research, 2004, 57: 1116-112

    James W. Taylor. An evaluation of methods for very short-term load forecasting using minute-by-minute British data, 2008. International Journal of Forecasting, 24 (4). pp. 645-658. ISSN 0169-2070

    K.Hornik, M.Stinchocombe, 1989. H.White, Multilayer feedforward networks are universal approximators,NeuralNetworks2 (1989) 359–366.

    Li, Q.S., Wu, J.R., 2007. Time–frequency analysis of typhoon effects on a 79-storey tall building. Journal of Wind Engineering and Industrial Aerodynamics 95 (12), 1648–1666.

    Liang, H., Lin, Q.-H., Chen, J.D.Z., 2005. Application of the empirical mode decomposition to the analysis of esophageal manometric data in gastro esophageal reflux disease. IEEE Transactions on Biomedical Engineering 52 (10), 1692–1701.

    Lean Yu, Shouyang Wang, Kin Keung Lai., 2008. Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Economics 30 (2008) 2623–2635.

    Lean Yu, ShouyangWanga, KinKeungLai, FenghuaWenc, 2010. A multiscale neural network learning paradigm for financial crisis forecasting. Neuro computing, 73:716-725

    Liu, K. Subbarayan, S. Shoults, R.R. Manry, M.T. Kwan, C. Lewis, F.I. Naccarino, J. Autom. & Robotics Res. Inst., Texas Univ., Arlington, TX, 1996. Comparison of very short-term load forecasting techniques. IEEE Transactions on Power Systems. Vol. 11. No. 2. May 1996

    Mirmirani, S. and H.C. L, 2004. Gold Price, Neural Networks and Genetic Algorithm, Computational Economics, 23, 193-200.

    Mendelsohn L., Preprocessing data for Neural Networks, 1993. Tech Anal Stocks Commod, 1993:52-58

    Mohsen Hayati and Yazdan Shirvany, 2007. Artificial Neural Network Approach for Short Term Load Forecasting for Illam Region. World Academy of Science, Engineering and Technology 28 2007

    N.X. Jia, R. Yokoyamaa, Y.C. Zhoub, Z.Y. Gaoc, 2001. A flexible long-term load forecasting approach based on new dynamic simulation theory — GSIM. International Journal of Electrical Power & Energy Systems Volume 23, Issue 7, October 2001, Pages 549-556

    Nahi Kandil, Rene´ Wamkeue, Maarouf Saad, Semaan Georges, 2006. An efficient approach for short term load forecasting using artificial neural networks. International Journal of Electrical Power & Energy Systems Volume 28, Issue 8, October 2006, Pages 525-530.

    Ray Ruichong Zhang, M.ASCE; Shuo Ma; Erdal Safak, M.ASCE; and Stephen Hartzell., 2003. Hilbert-Huang Transform Analysis of Dynamic and earthquake motion recordings. Journal of Engineering Mechanics, Vol. 129, No. 8, pp. 861-875.

    Ray Ruichong Zhang, Shuo Ma, and Stephen Hartzell., 2003. Signatures of the Seismic Source in EMD-Based Characterization of the 1994 Northridge, California, Earthquake Recordings. Bulletin of the Seismological Society of America; February 2003; v. 93; no. 1; p. 501-518.

    Ruqiang Yan, Student Member, IEEE, and Robert X. Gao, Senior Member,IEEE., 2006. Hilbert–Huang Transform-Based Vibration Signal Analysis for Machine Health Monitoring. IEEE Transactions on instrumentation and measurement, Vol. 55, No. 6.

    Ruey-Hsun Liang, Ching-Chi Cheng, 2002. Short-term load forecasting by a neuro-fuzzy based approach. International Journal of Electrical Power & Energy Systems Volume 24, Issue 2, February 2002, Pages 103-111

    Shahriar Shafiee and ErkanTopal, 2010. An overview of global gold market and gold price forecasting. Resources Policy Volume 35, Issue 3, September 2010, Pages 178-189.

    Stephen A. Baker and Roger C. van Tassel, 1985. Forecasting the price of gold: A fundamentalist approach Atlantic Economic Journal Volume 13, No. 4, 43-51

    Wu, Z., and N. E Huang, 2009. Ensemble Empirical Mode Decomposition: a noise-assisted data analysis method. Advances in Adaptive Data Analysis. Vol.1, No.1. 1-41.

    Wei SUN, 2010. Research on GA-SVM Model for Short Term Load Forecasting Based on LDM-PCA Technique. Journal of Computational Information Systems 6:10 (2010) 3183-3189.

    Yen-Rue Chang, 2011. Non-stationary time series analysis by using Hilbert-Huang transform: electricity consumption and gold price volatility. Master Thesis of Graduate Institute of Applied Physics, College of Science NCCU.
    Description: 碩士
    國立政治大學
    應用物理研究所
    98755011
    100
    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

    Files in This Item:

    File Description SizeFormat
    501101.pdf1296KbAdobe PDF681View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback