Abstract
Genetic programming (GP), a relatively young and growing branch of evolutionary computation is gradually proving to be a promising method of modelling complex prediction and classification problems. This paper evaluates the suitability of a linear genetic programming (LGP) technique to predict electricity demand in the State of Victoria, Australia, while comparing its performance with two other popular soft computing techniques. The forecast accuracy is compared with the actual energy demand. To evaluate, we considered load demand patterns for ten consecutive months taken every 30 minutes for training the different prediction models. Test results show that while the linear genetic programming method delivered satisfactory results, the neuro fuzzy system performed best for this particular application problem, in terms of accuracy and computation time, as compared to LGP and neural networks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
W. Banzhaf, P. Nordin, R.E. Keller, F.D. Francone, Genetic Programming — An Introduction — On The Automatic Evolution of Computer Programs and Its Applications, Morgan Kaufmann Publishers, Inc., 1998.
A. Abraham, B. Nath, A Neuro-Fuzzy Approach for Forecasting Electricity Demand in Victoria, Applied Soft Computing Journal, Elsevier Science, Volume 1 /2, 2001, pp. 127–138.
L.M. Deschaine, F.A. Zafran, J.J. Patel, D. Amick, R. Pettit, SAIC, F.D. Francone, P. Nordin, E. Dukes, L.V. Fausett, Solving the Unsolved-Using Machine Learningto Model a Complex Production Process-Case Example Applying Three Machine Learning Techniques, Society for Computer Simulation’s Advanced Simulation Technology Conference, Washington, DC, USA, April 2000.
J.R. Koza, F.H. Bennett, D. Andre, M.A. Keane, GENETIC PROGRAMMING-III — Darwinian Invention and Problem Solving, Morgan Kaufmann Publishers, Inc., 1999
F.D. Francone, P. Nordin, W. Banzhaf, E. DILKES, L.M. Deschaine, AIM LearningTM Adaptive, Real-Time, Control Technologies. Society for Computer Simulation’s Advanced Simulation Technology Conference, Washington, DC, USA, April 2000.
M. Brameier, W. Banzhaf, A comparison of linear genetic programming and neural networks in medical data mining, Evolutionary Computation, IEEE Transactions on, Volume: 5(1), 2001, pp. 17 —26.
John R.Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection.MIT Press, 1992.
The Victorian ESI Reforming the Victorian Electricity Supply Industry - 1995: Industry Review.
S. Haykin, Neural Networks: A Comprehensive Foundation, Second edition, Prentice Hall Inc, USA, 1999.
V. Cherkassky, Fuzzy Inference Systems: A Critical Review, Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications, Kayak O, Zadeh LA et al (Eds.), Springer, 1998, pp. 177–197.
D. Nauk, F. Klawonn, R Kruse, Foundations of Neuro Fuzzy Systems, John Willey & Sons, 1997.
N. Kasabov, Evolving Fuzzy Neural Networks-Algorithms, Applications and Biological Motivation, in Yamakawa T and Matsumoto G (Eds), Methodologies for the Conception, Design and Application of Soft Computing, World Scientific, 1998, pp. 271–274.
National Electricity Market Management Company Ltd: http://www.nemmco com.au/
AIMLearning Technology, http://www.aimlearning.com.
Kasabov, N. and Woodford B. Rule Insertion and Rule Extraction from Evolving Fuzzy Neural Networks: Algorithms and Applications for Building Adaptive, Intelligent Expert Systems, In Proceedings of the FUZZ-IEEE’99 International Conference on Fuzzy Systems, Seoul, Korea, 1999, pp. 14061411.
P. Nordin, A Compiling Genetic Programming System that Directly Manipulates the Machine Code, Advances in Genetic Programming, K. Kinnear (eds.), MIT Press, Cambridge, MA, 1994, pp. 311–331.
A. F. Moller, A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning, Neural Networks, Volume (6), 1993, pp. 525–533.
Zadeh LA, Roles of Soft Computing and Fuzzy Logic in the Conception, Design and Deployment of Information/Intelligent Systems, Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications, O Kaynak, LA Zadeh, B Turksen, IJ Rudas (Eds.), 1998, pp 1–9.
Mamdani E H and Assilian S, An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller, International Journal of Man-Machine Studies, Vol. 7 (1), 1975, pp. 1–13.
B. Nath and M. Nath, Using Neural Networks and Statistical Methods for Forecasting Electricity Demand in Victoria, International Journal of Management and Systems, Volume 16 (1), 2000, pp. 105–112.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bhattacharya, M., Abraham, A., Nath, B. (2002). A Linear Genetic Programming Approach for Modelling Electricity Demand Prediction in Victoria. In: Abraham, A., Köppen, M. (eds) Hybrid Information Systems. Advances in Soft Computing, vol 14. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1782-9_28
Download citation
DOI: https://doi.org/10.1007/978-3-7908-1782-9_28
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1480-4
Online ISBN: 978-3-7908-1782-9
eBook Packages: Springer Book Archive