Electricity Consumption Prediction Model using Neuro-Fuzzy System

In this paper the development of neural network based fuzzy inference system for electricity consumption prediction is considered. The electricity consumption depends on number of factors, such as number of customers, seasons, type-s of customers, number of plants, etc. It is nonlinear process and can be described by chaotic time-series. The structure and algorithms of neuro-fuzzy system for predicting future values of electricity consumption is described. To determine the unknown coefficients of the system, the supervised learning algorithm is used. As a result of learning, the rules of neuro-fuzzy system are formed. The developed system is applied for predicting future values of electricity consumption of Northern Cyprus. The simulation of neuro-fuzzy system has been performed.

• Rahib Abiyev
• Vasif H. Abiyev
• C. Ardil

• Fuzzy logic
• neural network
• neuro-fuzzy system
• neuro-fuzzy prediction.

[1] Yager R.R., Zadeh L.A.(Eds)."Fuzzy sets, neural networks and soft
computing", Van Nostrand Reinhold, New York, 1994.
[2] Kosko B,"Neural networks and fuzzy systems. A dynamical system
approach to machine intelligence", Prentice- Hall International Inc.,
Englewood Cliffs. 1993.
[3] Witold Pedryz, editor,"Fuzzy Modelling: Paradigms and Practice",
Kluwer Academic Publisher, Boston, 1996.
[4] Smaoui N. An Artificial Neural Network Noise Reduction Method for
Chaotic Attractors. Intern J. Computer Math., Vol.73,pp.417-431.
[5] Lapades A, Farber R. Nonlinear Signal Processing Using Neural
Networks: Prediction and Signal Modeling. Los Alamos. 1987.
[6] Ying-Qian Zhang, Lai-Wan Chan. Fourier Recurrent Networks for
Time series Prediction. Proceeding of International Conference on
Neural Information Processing, ICONIP 2000, Tacjon, Korea, pp 576-
582, 2000.
[7] Nunnari G, Nucifora A, Randieri C. The application of neural
techniques to the modeling of time series of atmospheric pollution
data. Ecological Modelling 111; 187-205, 1998.
[8] Tang Z., de Almeida C., Fishwick P.A. Time-series forecasting using
neural network versus Box-Jenkins methodology. Simulation, 57, pp.
303-310, 1991.
[9] Rahib Abiyev, "Controllers based on Soft computing elements",
Electrical, Electronics and Computer Engineering Symposium NEUCEE2001
and Exhibition,. Nicosia, TRNC, Turkey, pp.182-188, May,
2001.