Modeling and Empirical Evaluation of Machine Learning Based Load Forecasting Models for Pakistan

Authors

  • S. M. Awan Department of Computer Science and Engineering, University of Engineering and Technology, Lahore. Pakistan. Al-Khawarizmi Institute of Computer Science, University of Engineering and Technology, Lahore. Pakistan.
  • M. Aslam Department of Computer Science and Engineering, University of Engineering and Technology, Lahore. Pakistan.
  • Z. A. Khan Department of Electrical Engineering, University of Engineering and Technology, Lahore. Pakistan
  • A. Saleem Department of Computer Science and Engineering, University of Engineering and Technology, Lahore. Pakistan.

Abstract

Electric load forecasting (LF) deals with predicting futuristic energy demand of consumers. It is the foremost and important step of energy distribution and generation planning. Machine learning based statistical and artificial intelligence techniques are widely used for LF. Among these, artificial neural networks (ANN) and support vector machines (SVM) emerge as competitive modeling approaches for LF. To further improve the performance of these models, optimization techniques are being used to formulate hybrid LF models. Availability of modern approaches motivated authors to solve the issues with power planning in Pakistan. Hence, we contribute towards proposing machine learning based accurate model of LF on Pakistan power system data set. Several forecasting models are formed using hybrid optimization and model development techniques, which are ranked against their forecasting accuracy and performance. SVM based models performed well and achieved 98.91% accuracy of forecasts. On the other hand, ANN based models showed comparable performance achieving 98.34% accuracy with added ability to avoid over-fitting, and efficiency with improved results.

References

L. Suganthiand A.A Samuel, Renewable and Sustainable Energy

Reviews 16 (2012) 1223.

H.K.Alfares and M.Nazeeruddin, Int. J. Sys. Sci. 33 (2010) 23.

H.Hahn, S.Meyer-Nieberg and S.Pickl, European Journal of

Operational Research 199 (2009) 902.

C. Xia, J. Wang and K. McMenemy, International Journal of

Electrical Power & Energy Systems 32 (2010) 743.

E.E. Elattar, J. Goulermas and Q.H. Wu, IEEE Transactions on

Applications and Reviews 40 (2010) 438.

N. Amjady and F. Keynia, Energies 4 (2011) 488.

J. Che, J. Wang and G. Wang, Energy 37 (2012) 657.

A. Kavousi-Fard, Journal of Experimental & Theoretical Artificial

Intelligence 25 (2013) 543.

S. Haykin, Neural Networks: A Comprehensive Foundation, Chapter

-7, 2nd edn. Prentice Hall PTR, NJ, USA (1999) 190.

V.N.Vapnik, The Nature of Statistical Learning Theory, Chapter 5-6,

Springer-Verlag, NY, USA (1995) 131.

O. Chapelle, V. Vapnik, O. Bousquet and S. Mukherjee, Machine

Learning 46 (2002) 131.

S. Sra, S. Nowozin and S.J. Wright, Optimization for Machine

Learning, Chapter 1, MIT Press (2011) 5.

A.S. Ahmad, M.Y. Hassan, M.P. Abdullah, H.A. Rahman,

F. Hussin, H. Abdullah and R. Saidur, Renewable and Sustainable

Energy Reviews 33 (2014) 102.

G. OÄŸcu, O.F. Demirel and S. Zaim, Social and Behavioral Sciences

(2012) 1576.

L.J. Soares and M.C. Medeiros, Int. J. Forecast. 24 (2008) 630.

J.W.Taylor, and P.E. McSharry, IEEE Transactions on Power

Systems 22 (2007) 2213.

B.M.Wilamowski and H.Yu, IEEE Transactions on Neural

Networks 21 (2010) 930.

L.M.Saini and M.K. Soni, Generation, Transmission and

Distribution, IEEE Proceedings 149 (2002) 578.

H.A.O. Junior, L. Ingber, A. Petraglia, M.R. Petraglia and M.A.S.

Machado, Adaptive Simulated Annealing, Stochastic Global

Optimization and Its Applications with Fuzzy Adaptive Simulated

Annealing 35 (2012) 33, Springer.

P.-F.Pai and W.C.Hong, Energy Conversion and Management 46

(2005) 2669.

J. Kennedy, Particle Swarm Optimization, Encyclopedia of Machine

Learning (2010) 760.

Y. Huang, D. Li, L. Gao and H. Wang, Proc. IEEE Conf. on Control

and Decision 1 (2009) 1448.

L.M. Saini, Electric Power Systems Research 78 (2008) 1302.

X.-S.Yang, S.S.S. Hosseini and A.H.Gandomi, Applied Soft

Computing 12 (2012) 1180.

D. Karaboga and C. Ozturk, Applied Soft Computing 11 (2011) 652.

W.-C.Hong, Energy 36 (2011) 5568.

Z. Hu, Y. Bao and T. Xiong, The Scientific World Journal 1

(2013)10.

A.U. Haque, P. Mandal, J. Meng and R.L. Pineda, Procedia

Computer Science 12 (2012) 320.

R.J. Hyndman and A.B. Koehler, Int. J. Forecast. 22 (2006) 679.

Downloads

Published

23-10-2014

How to Cite

[1]
S. M. Awan, M. Aslam, Z. A. Khan, and A. Saleem, “Modeling and Empirical Evaluation of Machine Learning Based Load Forecasting Models for Pakistan”, The Nucleus, vol. 51, no. 4, pp. 405–410, Oct. 2014.

Issue

Section

Articles

Most read articles by the same author(s)