DEMAND FORECASTING OF INDUSTRIAL ELECTRICAL ENERGY CONSUMPTION FOR TAMILNADU STATE

Authors

  • Senthilkumar T Department of Mechanical of Engineering, Mahendra Institute of Engineering and Technology, Mallasamudram, Namakkal-637503, Tamil Nadu, India
  • Venkatesh R Department of Mechanical of Engineering, Mahendra Institute of Engineering and Technology, Namakkal, Tamil Nadu- 637503, India.
  • Sam Charles J Department of Mechanical of Engineering, Mahendra Institute of Engineering and Technology, Namakkal, Tamil Nadu- 637503, India.
  • Senthil P Department of Mechanical of Engineering, Mahendra Engineering college, Mallasamudram, Namakkal-637503, Tamil Nadu, India.
  • Praveen kumar V Department of Mechanical of Engineering, Mahendra Engineering college, Mallasamudram, Namakkal-637503, Tamil Nadu, India.

DOI:

https://doi.org/10.37255/jme.v4i2pp093-096

Keywords:

Forecasting, Energy consumption, ANN (Artificial Neural Network), Long term forecasting, MLRM (Multiple Linear Regression Model)

Abstract

Energy consumption forecasting is vitally important for the deregulated electricity industry in India, particularly in Tamilnadu state. A large variety of mathematical methods have been developed for energy forecasting. In this study, historical data set including population (POP), Gross state domestic Product (GSDP), Yearly peak demand (YPD), and Per Capita income (PCI) were considered from the year 2005 to 2011.Firstly, the multiple linear regression model (MLRM)has been developed. The regression model outputs were optimized using Neural network method.

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References

Azadeh A and Tarverdian S (2007), “Integration of genetic algorithm, computer simulation and design of experiments for forecasting electrical energy consumption”, Energy Policy Vol.35, 5229-5241.

Amjadi M H, Nezamabadi-pour H and Farsangi M M (2010), “Estimation of electricity demand of Iran using two heuristic algorithms”, Energy conversion and management Vol.51,493-497.

Assareh E, Behrang M A, Asssari M R and Ghanbarzadeh A (2010), “Application of PSO and GA techniques on demand estimation of oil in Iran”, Energy Vol.35, 5223-5229.

Azadeh, Ghaderi S F, Tarverdian S and Saber M (2007), “Integration of artificial neural networks and genetic algorithm to predict electrical energy consumption”, Applied mathematics and computation Vol.186, 1731-1741.

Didem Cinar, Gulgun Kayakutlu and Tugrul Daim (2010), “Development of future energy scenarios with intelligent algorithms: Case of hydro in Turkey”, Energy Vol.35,1724-1729.

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Published

2019-06-01

Issue

Section

Articles

How to Cite

[1]
“DEMAND FORECASTING OF INDUSTRIAL ELECTRICAL ENERGY CONSUMPTION FOR TAMILNADU STATE”, JME, vol. 14, no. 2, pp. 093–096, Jun. 2019, doi: 10.37255/jme.v4i2pp093-096.

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