Please use this identifier to cite or link to this item:
Forecasting electricity consumption as well as gold price by using an EEMD-based Back-propagation Neural Network Learning Paradigm
Tsai, Yu Ching
Shiau, Yuo Hsien
Tsai, Yu Ching
Ensemble Empirical Mode Decomposition
Back-propagation Neural Network
electricity consumption forecasting
gold price forecasting
very-short term load forecasting
|Issue Date: ||2013-09-04 15:28:15 (UTC+8)|
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.
|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:
All items in 政大典藏 are protected by copyright, with all rights reserved.