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Fuzzy wavelet neural network for prediction of electricity consumption

Published online by Cambridge University Press:  13 November 2008

Rahib H. Abiyev
Rahib H. Abiyev
Affiliation:
Department of Computer Engineering, Near East University, Mersin, Turkey
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Abstract

The development of a fuzzy wavelet neural network (FWNN) for the prediction of electricity consumption is presented. The fuzzy rules that contain wavelets are constructed. Based on these rules, the structure of FWNN-based system is described. The FWNN system is applied for modeling and prediction of complex time series. The gradient algorithm and genetic algorithm are used for learning of FWNN parameters. The developed FWNN is applied for prediction of electricity consumption. This process has high-order nonlinearity. The statistical data for the last 10 years are used for the development of FWNN prediction model. The effectiveness of the proposed system is evaluated with the results obtained from the simulation of FWNN-based prediction system and with the comparative simulation results of previous related models.

Keywords

Fuzzy Wavelet Neural NetworkNeurofuzzy ModelingPrediction of Electricity ConsumptionTime Series PredictionWavelet Network
Type
Research Article
Information
AI EDAM , Volume 23 , Issue 2 , May 2009 , pp. 109 - 118
Copyright
Copyright © Cambridge University Press 2008

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References

REFERENCES

Abdel-Aal, R.E., & Al-Garni, A.Z. (1997 a). Forecasting monthly electric energy consumption in eastern Saudi Arabia using univariate time series analysis. Energy 22, 1059–1069.Google Scholar
Abdel-Aal, R.E., & Al-Garni, A.Z. (1997 b). Forecasting monthly electric energy consumption in eastern Saudi Arabia using abductives networks. Energy 22, 911–921.Google Scholar
Abiyev, R.H. (2005). Controller based of fuzzy wavelet neural network for control of technological processes. CIMSA 2005, IEEE Int. Conf. Computational Intelligence for Measurement Systems and Applicationspp. 215 –219, Giardini Naxos, Italy.Google Scholar
Abiyev, R.H. (2006). Time series prediction using fuzzy wavelet neural network model. ICANN 2006, Part II. Lecture Notes in Computer Sciences, Vol. 4132. Berlin: Springer–Verlag, pp. 191–200.Google Scholar
Al-Shehri, A. (1999). Artificial neural network for forecasting residential electrical energy. International Journal of Energy Research 23, 649–661.3.0.CO;2-T>CrossRefGoogle Scholar
Ang, B.W., & Ng, T.T. (1992). The use of growth curves in energy studies. Energy 17, 25–36.CrossRefGoogle Scholar
Box, G.E.P., Jenkins, G.M., & Reinsel, G.C. (1994). Time Series Analysis, Forecasting and Control, 3rd ed.Englewood Cliffs, NJ: Prentice–Hall.Google Scholar
Cao, L., Hong, Y., Fang, H., & He, G. (1995). Predicting chaotic time series with wavelet networks. Physica D 85, 225–238.Google Scholar
Ceylan, H., & Ozturk, H. (2004). Estimating of energy demand of Turkey based on economic indicators using genetic algorithm approach. Energy Conversion and Management 45, 2525–2537.CrossRefGoogle Scholar
Chang, P.R., Weihui, Fu, & Minjun, Yi. (1998). Short term load forecasting using wavelet networks. Engineering Intelligent Systems for Electrical Engineering and Communications 6, 217–230.Google Scholar
Chen, Y., Yang, B., Dong, J., & Abraham, A. (2005). Time series forecasting using flexible neural tree model. Information Sciences 174 (3–4), 219–235.Google Scholar
Crowder, R.S III, (1990). Predicting the Mackey–Glass time series with cascade correlation learning. Proc. 1990 Connectionist Models Summer School (Touretzky, D., Hinton, G., & Sejnowski, T., Eds.), pp. 117–123. Pittsburgh, PA: Carnegie Mellon University.Google Scholar
Daniel, W.C.H., Ping-An, Z., & Jinhua, X. (2001). Fuzzy wavelet networks for function learning. IEEE Transactions on Fuzzy Systems 9 (1), 200–211.Google Scholar
Egelioglu, F., Mohamad, A.A., & Guven, H. (2001). Economic variables and electricity consumption in Northern Cyprus. Energy 26, 355–362.Google Scholar
Goldberg, D.E. (1998). Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison–Wesley.Google Scholar
Hill, T., O'Connor, M., & Remus, W. (1994). Artificial neural network models for forecasting and decision making. International Journal of Forecasting 10, 5–15.CrossRefGoogle Scholar
Hsu, C.C., & Chen, C.Y. (2003). Regional load forecasting in Taiwan—applications of artificial neural networks. Energy Conversion and Management 44, 1941–1949.CrossRefGoogle Scholar
Jang, J.-S.R. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics 23 (3), 665–683.Google Scholar
Jang, J.-S.R. (1997). Neuro-Fuzzy and Soft Computing. A Computational Approach to Learning and Machine Intelligence. Englewood Cliffs, NJ: Prentice–Hall.Google Scholar
Khao, T.Q.D., Phuong, L.M., Binh, P.T.T., & Lien, N.T.H. (2004). Application of wavelet and neural network to long-term load forecasting. Int. Conf. Power System Technology, POWERCON 2004pp. 840–844, Singapore.CrossRefGoogle Scholar
Kugarajah, T., & Zhang, Q. (1995). Multidimensional wavelet frames. IEEE Transactions on Neural Networks 6, 1552–1556.Google Scholar
Lapades, A., & Farber, R. (1987). Nonlinear Signal Processing Using Neural Networks: Prediction and Signal Modeling, Vol. 87545. Technical Report. Los Alamos, NM: Los Alamo National Lab.Google Scholar
Lin, C.K., & Wang, S.D. (1996). Fuzzy modelling using wavelet transform. Electronics Letters 32, 2255–2256.CrossRefGoogle Scholar
Lin, Y., & Wang, F.Y. (2005). Predicting chaotic time series using adaptive wavelet-fuzzy inference system. Proc. IEEE Intelligent Vehicles Symposium, pp. 888–893.CrossRefGoogle Scholar
Maddala, G.S. (1996). Introduction to Econometrics. Englewood Cliffs, NJ: Prentice–Hall.Google Scholar
Nunnari, G., Nucifora, A., & Randieri, C. (1998). The application of neural techniques to the modeling of time series of atmospheric pollution data. Ecological Modelling 111, 187–205.Google Scholar
Guo, Q.J., Yu, H.B., & Xu, A.D. (2005). Wavelet fuzzy network for fault diagnosis. Proc. Int. Conf. Communications, Circuits and Systems, pp. 993–998.Google Scholar
Rajan, M., & Jain, V.K. (1999). Modelling of electrical energy consumption in Delhi. Energy 24, 351–361.Google Scholar
Saab, S., Badr, E., & Nasr, G. (2001). Univariate modelling and forecasting of energy consumption: the case of electricity in Lebanon. Energy 26, 1–14.CrossRefGoogle Scholar
Smaoui, N. (2000). An artificial neural network noise reduction method for chaotic attractors. International Journal of Computer Mathematics 73, 417–431.Google Scholar
Szu, H., Telfer, B., & Garcia, J. (1996). Wavelet transforms and neural networks for compression and recognition. Neural Networks 9, 695–708.CrossRefGoogle Scholar
Thuillard, M. (2000). Fuzzy logic in the wavelet framework. Proc. Toolmet 2000April 13–14Oulu.Google Scholar
Thuillard, M. (2001).Wavelets in Softcomputing. Singapore: World Scientific Press.Google Scholar
Yager, R.R., & Zadeh, L.A., Eds. (1994). Fuzzy Sets, Neural Networks and Soft Computing. New York: Van Nostrand Reinhold.Google Scholar
Yan, Y.Y. (1998). Climate and residential electricity consumption in Hong Kong. Energy 23 (1), 17–20.CrossRefGoogle Scholar
Yao, A.W.L., & Chi, S.C. (2004). Analysis and design of a Taguchi–Grey based electricity demand predictor for energy management systems. Energy Conversion and Management 45, 1205–1217.CrossRefGoogle Scholar
Yao, A.W.L., Chi, S.C., & Chen, J.H. (2003). An improved grey-based approach for electricity demand forecasting. Electric Power Systems Research 67, 217–214.CrossRefGoogle Scholar
Zhang, J., Walter, G.G., & Wayne Lee, W.N. (1995). Wavelet neural networks for function learning. IEEE Transaction on Signal Processing 43 (6), 1485–1497.Google Scholar
Zhang, Q., & Benviste, A. (1995). Wavelet networks. IEEE Transactions on Neural Networks 3, 889–898.Google Scholar
Zhang, Y.Q., & Chan, L.W. (2000). Fourier recurrent networks for time series prediction. Proc. Int. Conf. Neural Information Processing, ICONIP 2000pp. 576–582, Tacjon, Korea.Google Scholar