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

• S. Saravanan, S. Kannan, and C. Thangaraj, "Forecasting India's Electricity Demand Using Artificial Neural Network ", IEEE International conference 2012.
• 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.
• 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.
• 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.
• Manish Sankar, B. Yeganarayana, Deepak Khemani "Backpropagation learning algorithms for classification with fuzzy mean square error" Elsevier 1998.
• RustumMamlook,OmarBadran ,EmadAbdulhadi, "A fuzzy inference model for short-term load forecasting " a Middle East University for Graduate Studies, Elsevier 2009.
• 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.
• 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.
• Bahman Kermanshahi, Hiroshi Iwamiya, Enviroinmental Engineering," Up to year 2020 load forecasting using neural nets", Elsevier 2002.
• Paras Mandal, Tomonobu Senjyu, Naomitsu Urasaki, Toshihisa Funabashi, "A neural network based several-hour-ahead electric load forecasting using similar days approach" Elsevier 2006.
• 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.
• 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.
• http://www. populstat. info/
• http://www. indexmundi. com/india/gdp_(purchasing_power_parity). html
• http://www. iasri. res. in/agridata/12data/chapter1/db2012tb1_13. pdf