Top > JACIII > Neuro-Fuzzy Approaches for Forecasting Electrical Load Using A ...

single-jc.php

JACIII Vol.12 No.4 pp. 361-369
doi: 10.20965/jaciii.2008.p0361
(2008)

Paper:

Views over last 60 days: 374

Neuro-Fuzzy Approaches for Forecasting Electrical Load Using Additional Moving Average Window Data Filter on Takagi-Sugeno Type MISO Networks

Felix Pasila*, Ajoy K. Palit**, and Georg Thiele***

*Electrical Engineering Department, Petra Christian University, Siwalankerto 121-131, Surabaya, Indonesia

**Department of Electrical Engineering, ITEM, University of Bremen, D-28359 Bremen, Germany

***Department of Electrical Engineering, IAT, University of Bremen, D-28359 Bremen, Germany

Received:
April 23, 2007
Accepted:
June 27, 2007
Published:
July 20, 2008
Creative Commons License
Keywords:
TS model MISO-NF network, moving average filter, Gustafson-Kessel clustering algorithm, accelerated Levenberg-Marquardt algorithm
Abstract
The paper describes a neuro-fuzzy approach with additional moving average window data filter and fuzzy clustering algorithm that can be used to forecast electrical load using the Takagi-Sugeno (TS) type multi-input single-output (MISO) neuro-fuzzy network efficiently. The training algorithm with additional moving average filter is efficient in the sense that it can bring the performance index of the network, such as the sum squared error (SSE), down to the desired error goal much faster than the simple Levenberg-Marquardt algorithm (LMA). The fuzzy clustering algorithm allows the selection of initial parameters of fuzzy membership functions, e.g. mean and variance parameters of Gaussian membership functions of neuro-fuzzy networks, which are otherwise selected randomly. The initial parameters of fuzzy membership functions, which result in low SSE value with given training data of neuro-fuzzy network, are further fine tuned during the network training. Finally, the above training algorithm is tested on TS type MISO neuro-fuzzy structure for long-term forecasting application of electrical load time series.
Cite this article as:
F. Pasila, A. Palit, and G. Thiele, “Neuro-Fuzzy Approaches for Forecasting Electrical Load Using Additional Moving Average Window Data Filter on Takagi-Sugeno Type MISO Networks,” J. Adv. Comput. Intell. Intell. Inform., Vol.12 No.4, pp. 361-369, 2008.
Data files:
References
  1. [1] J. S. R. Jang, “ANFIS: Adaptive network Based Fuzzy Inference System,” IEEE T. SMC., 23(3): pp. 665-685, 1993.
  2. [2] A. K. Palit and R. Babuška, “Efficient training algorithm for Takagi-Sugeno type Neuro-Fuzzy network,” Proc. of FUZZ-IEEE, Vol.3, pp. 1538-1543, 2001.
  3. [3] A. K. Palit, G. Doeding, W. Anheier, and D. Popovic, “Backpropagation based training algorithm for Takagi-Sugeno type MIMO neuro-fuzzy network to forecast electrical load time series,” Proc. of FUZZ-IEEE, Honolulu, Hawai, USA. Vol.1, pp. 86-91, 2002.
  4. [4] A. K. Palit and D. Popovic, “Computational Intelligence in Time Series Forecasting, Theory and Engineering Applications,” Springer-Verlag, London, pp. 230-246, 2005.
  5. [5] F. Pasila, “Forecasting of Electrical Load using Takagi-Sugeno type MIMO Neuro-Fuzzy network,” Master Thesis, University of Bremen, Germany, 2006.
  6. [6] M. Setnes, R. Babuška, and U. Kaymark, “Similarity measures in Fuzzy rule base simplification,” IEEE T. System, Man and Cybernetics, Vol.28, pp. 771-775, 1998.
  7. [7] D. Xiaosong, D. Popovic, and G. Schulz-Ekloff, “Oscillation resisting in the learning of Backpropagation neural networks,” Proc. of 3rd IFAC/IFIP, Belgium, 1995.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 18, 2024