Load Forecasting for Economic Power Generation and Distribution in Smart Grid Environment

Authors

  • Sastry AB Dept. of Computer Science and Engineering, SET, Jain University, Bangalore
  • N Satish Kumar Dept. of Computer Science and Engineering, SET, Jain University, Bangalore
  • CR Manjunath Dept. of Computer Science and Engineering, SET, Jain University, Bangalore
  • C Prasanna Kumar Dept. of Electrical and Electronics Engineering, SET, Jain University, Bangalore

Keywords:

Data Mining, Load Forecasting, Regression, Smart Grid, RMSE

Abstract

Accurate Load Forecasting of electricity demand is vital for any power utility to reduce losses and to increase efficiency. This is vital for implementation of Smart Grid across the country as Load Forecasting helps to provide reliable and quality power supply to the consumer. In this paper we shall compare some of the existing methods that are used for load forecasting and compare the performance of each of those methods using the RMS E value to measure the performance. The method with the least RMS E value performs better having reliable level of accuracy. We have considered the holidays and temperature of the area information also in our work as it greatly influences the load consumption pattern among the consumers.

References

Towards Accurate Electricity Load Forecasting in Smart Grids by Zeyar Aung, Mohamed Toukhy et al. -2012

Forecasting Electricity Consumption: A Comparison of Models for New Zealand by Zaid Mohamed and Pat Bodger

Bianco V, Manca O, Nardini S. Electricity consumption forecasting in Italy using linear regression models. Energy 2009; 34(9):1413e21.

Benaoudaa D, Murtaghb F, Starckc JL, et al. Waveletbased nonlinear Multi scale decomposition model for electricity load forecasting. Euro computing 2006;70 (1):139–54.

Pappas SS, Ekonomou L, Karamousantas DC, et al. Electricity demand loads modelling using autoregressive moving average (ARMA) models. Energy 2008; 33(9):1353–60.

Christiaanse W. Short-term load forecasting using general exponential smoothing. Power Apparatus Syst IEEE Trans 1971;PAS-90(2):900–11.

T. Cipra, “Robust exponential s moothing,” Int. J. Forecast., vol. 11, no. 1, pp. 57–69, 1992.

Gelper, S., Fried, R., Croux, C., 2010. Robust forecasting with exponential and Holt winters smoothing. J. Forecast. 29, 285–300.

(2013 ) The Weka Website [Online]. Available: http://cs.waikato.com/.

Prayad Boonkham, Somsak SurapatpichaiA New Method for Electric Consumption Forecasting in a Semiconductor Plant

J. Shafer, R. Agrawal, and M. Mehta. SPRINT : A scalable parallel classifier for datamining. VLDB‟2001

L. Kaufman and P. J. Rousseeuw. Finding Groups in Data: an Introduction to Cluster Analysis. John Wiley & Sons, 2005

G. M. Jenkins, “Practical experiences with modeling and forecast”, Time Series, 1979.

H. T. Yang and C. M. Huang, “A new short-term load forecasting approach using self-organizing fuzzy ARMAX models”, IEEE Trans. Power Systems, vol.13, no.1, pp.217–225, 1998.

H. T. Yang, C. M. Huang and C. L. Huang, “Identification of ARMAX model for short term load forecasting: An evolutionary programming approach”, IEEE Trans. Power Systems, vol.11, no.1, pp.403–408, 1996.

Z. Yu, “A temperature match based optimization method for daily load prediction considering DLC effect”, IEEE Trans. Power Systems, vol.11, no.2, pp.728–733, 1996.

W. Charytoniuk, M. S. Chen and P. Van Olinda, “Non parametric regression based short-term load forecasting”, IEEE Trans. Power Systems, vol.13, no.3, pp.725–730,1998.02004006008001000120014001600180015K 30K 45 60KLinearRegressionLeastMedSqMultiLayerPerceptron

A. Harvey and S. J. Koopman, “Forecasting hourly electricity demand using time-varying splines”, J. American Stat. Assoc., vol.88, no.424, pp.1228–1236,1993.

J. W. Taylor and S. Majithia, “Using combined forecast swith changing weights for electricity demand profiling”, J. Oper. Res. Soc., vol.51, no.1, pp.72–82, 2000.

Downloads

Published

2024-02-26

How to Cite

Sastry, A. B. B., N, S. K., CR, M., & C, P. K. (2024). Load Forecasting for Economic Power Generation and Distribution in Smart Grid Environment. COMPUSOFT: An International Journal of Advanced Computer Technology, 3(07), 1030–1033. Retrieved from https://ijact.in/index.php/j/article/view/181

Issue

Vol. 3 No. 07 (2014): July

Section

Original Research Article

License

Copyright (c) 2014 COMPUSOFT: An International Journal of Advanced Computer Technology

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

©2023. COMPUSOFT: AN INTERNATIONAL OF ADVANCED COMPUTER TECHNOLOGY by COMPUSOFT PUBLICATION is licensed under a Creative Commons Attribution 4.0 International License. Based on a work at COMPUSOFT: AN INTERNATIONAL OF ADVANCED COMPUTER TECHNOLOGY. Permissions beyond the scope of this license may be available at Creative Commons Attribution 4.0 International Public License.