ANN and ANFIS for Short Term Load Forecasting

  • 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
Keywords: load forecasting, neural network, adaptive neuro fuzzy interface system

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.

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References

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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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