ANN and ANFIS for Short Term Load Forecasting

J. Chakravorty; S. Shah; H. N. Nagraja

doi:10.48084/etasr.1968

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

Corresponding author: S. Shah

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

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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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