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Neural-Fuzzy Approach for Power Load Forecasting Analysis

by J. Kumaran, G. Ravi, R. Rajkumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Number 16
Year of Publication: 2013
Authors: J. Kumaran, G. Ravi, R. Rajkumar
10.5120/12048-8116

J. Kumaran, G. Ravi, R. Rajkumar . Neural-Fuzzy Approach for Power Load Forecasting Analysis. International Journal of Computer Applications. 69, 16 ( May 2013), 31-35. DOI=10.5120/12048-8116

@article{ 10.5120/12048-8116,
author = { J. Kumaran, G. Ravi, R. Rajkumar },
title = { Neural-Fuzzy Approach for Power Load Forecasting Analysis },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 69 },
number = { 16 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 31-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume69/number16/12048-8116/ },
doi = { 10.5120/12048-8116 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:30:27.417770+05:30
%A J. Kumaran
%A G. Ravi
%A R. Rajkumar
%T Neural-Fuzzy Approach for Power Load Forecasting Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 69
%N 16
%P 31-35
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents Neuro-Fuzzy approach for forecasting analysis in power load. Forecasting the power load is a difficult task for a country and both positive and negative load forecasting makes a big problem for the country. An approach that Neuro-Fuzzy model is proposed for forecast power load in this paper. The proposed model a fuzzy back propagation network is constructed and then a fuzzy intersection is applied and after that de-fuzzify the result to generate a crisp value by using Radial Basis Function network (RBF). The proposed model improves the accuracy of power load forecasting. The forecasted results obtained by neuro-fuzzy method were compared with the Artificial Neural Network by using Mean Absolute Percentage Error (MAPE) to measure accuracy of the result. The experimental result shows that the neuro-fuzzy implementations have more accuracy.

References
  1. S. Saravanan, S. Kannan, and C. Thangaraj, "Forecasting India's Electricity Demand Using Artificial Neural Network ", IEEE International conference 2012.
  2. K. Hornik, M. Stinchcombe, and H. White, "Multilayer feed forward networks are universal approximators," in the proceeding of the IEEE Transaction on Neural Networks, vol. 2, no. 5, pp. 359–366, Oct 1995.
  3. K. Hornik, "Approximation capabilities of multilayer feed forward networks," in the proceeding of the IEEE Transaction on Neural Networks, vol. 4, no. 2, pp. 251–257, Jul 1991.
  4. M. Leshno, V. Y. Lin, A. Pinkus, and S. Schocken, "Multilayer feed forward networks with a no polynomial activation function can approximate any function," in the proceeding of the IEEE Transaction on Neural Networks, vol. 6, no. 6, pp. 861–867, Aug 1993.
  5. Manish Sankar, B. Yeganarayana, Deepak Khemani "Backpropagation learning algorithms for classification with fuzzy mean square error" Elsevier 1998.
  6. RustumMamlook,OmarBadran ,EmadAbdulhadi, "A fuzzy inference model for short-term load forecasting " a Middle East University for Graduate Studies, Elsevier 2009.
  7. D K Ranaweera, N F Hubele and G G Karady, "Fuzzy logic for short term load forecasting" in Elsevier transacting on Electrical & Energy Systems Vol. 18 No, pp. 215-222,1996.
  8. A. Ghanhari, A. Naghavi, S. F. Ghaderi, and M. Sabaghian, "Artificial Neural Networks and regression approaches comparison for forecasting Iran's annual electricity load," Proceeding International Conference on Power.
  9. Bahman Kermanshahi, Hiroshi Iwamiya, Enviroinmental Engineering," Up to year 2020 load forecasting using neural nets", Elsevier 2002.
  10. Paras Mandal, Tomonobu Senjyu, Naomitsu Urasaki, Toshihisa Funabashi, "A neural network based several-hour-ahead electric load forecasting using similar days approach" Elsevier 2006.
  11. chih-chou chiu, deborah f. cook , jen-lung kao and yu-chao chou " Combining a neural network and a rule-based expert system for short-term load forecasting " Elsevier 1997.
  12. Stephan Trenn "Multilayer Perceptrons: Approximation Order and Necessary Number of Hidden Units" in the proceeding of the IEEE Transaction on Neural Networks, Vol. 19. No. 5, MAY 2008.
  13. http://www. populstat. info/
  14. http://www. indexmundi. com/india/gdp_(purchasing_power_parity). html
  15. http://www. iasri. res. in/agridata/12data/chapter1/db2012tb1_13. pdf
Index Terms

Computer Science
Information Sciences

Keywords

Artificial Neural Network Load forecasting Neuro-fuzzy model Radial basis function network

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