ANN and ANFIS for Short Term Load Forecasting

Authors

  • J. Chakravorty Electrical Engineering Department, Indus University, Ahmedabad, India http://orcid.org/0000-0003-0892-0453
  • S. Shah Electrical Engineering Department, Indus Institute of Technology & Engineering, Indus University, Ahmedabad, India
  • H. N. Nagraja Electrical Engineering Department, Graphic Era University, Uttarakhand, India
Volume: 8 | Issue: 2 | Pages: 2818-2820 | April 2018 | https://doi.org/10.48084/etasr.1968

Abstract

Load forecasting has become one of the major areas of research in electrical engineering. Short term load forecasting (STLF) is essential for power system planning and economic load dispatch. A variety of mathematical methods has been developed for load forecasting. This paper discusses the influencing factors of STLF and an artificial intelligence (AI) based STLF model for MGVCL load. It also includes comparison of various AI models. Our main objective is to develop the best suited model for MGVCL, by critically evaluating the ways in which the AI techniques proposed are designed and tested.

Keywords:

load forecasting, neural network, adaptive neuro fuzzy interface system

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References

K. Kalaitzakis, G. S. Stavrakakis, E. M. Anagnostakis, “Short-term load forecasting based on artificial neural networks parallel implementation”, Electric Power Systems Research, Vol. 63, No. 3, pp. 185-196, 2002 DOI: https://doi.org/10.1016/S0378-7796(02)00123-2

M. Markou, E. Kyriakides, M. Polycarpou “24-Hour Ahead Short Term Load Forecasting Using Multiple MLP’, International Workshop on Deregulated Electricity Market Issues in South-Eastern Europe, pp. 1-6, 2008

E. Banda, K. A. Folly, “Short-Term Load Forecasting Using Artificial Neural Network”, IEEE Lausanne Power Tech, Lausanne, Switzerland, pp. 108-112, July 1-5, 2007 DOI: https://doi.org/10.1109/PCT.2007.4538301

Z. Souzanchi-K, H. Fanaee-T, M. Yaghoubi, M. R. Akbarzadeh-T, “A Multi Adaptive Neuro Fuzzy Inference System for Short Term Load Forecasting by Using Previous Day Features”, International Conference on Electronics and Information Engineering, Kyoto, Japan, Vol. 2, pp. 54-57, August 1-3, 2010 DOI: https://doi.org/10.1109/ICEIE.2010.5559714

H. P. Oak, S. J. Honade, ‘ANFIS Based Short Term Load Forecasting’, International Journal of Current Engineering and Technology, Vol. 5, No.3, pp. 1878-1880, 2015

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How to Cite

[1]
J. Chakravorty, S. Shah, and H. N. Nagraja, “ANN and ANFIS for Short Term Load Forecasting”, Eng. Technol. Appl. Sci. Res., vol. 8, no. 2, pp. 2818–2820, Apr. 2018.

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