A Linear Genetic Programming Approach for Modeling Electricity Demand Prediction in Victoria

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.

[1]  E. H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Man Mach. Stud..

[2]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[3]  Peter Nordin,et al.  A compiling genetic programming system that directly manipulates the machine-code , 1994 .

[4]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[5]  Detlef Nauck,et al.  Foundations Of Neuro-Fuzzy Systems , 1997 .

[6]  Peter Nordin,et al.  Genetic programming - An Introduction: On the Automatic Evolution of Computer Programs and Its Applications , 1998 .

[7]  Lotfi A. Zadeh,et al.  Roles of Soft Computing and Fuzzy Logic in the Conception, Design and Deployment of Information/Intelligent Systems , 1998 .

[8]  Wolfgang Banzhaf,et al.  A comparison of linear genetic programming and neural networks in medical data mining , 2001, IEEE Trans. Evol. Comput..

[9]  V. Cherkassky.  Fuzzy Inference Systems: A Critical Review , 1998 .

[10]  John R. Koza,et al.  Genetic Programming III - Darwinian Invention and Problem Solving , 1999, Evolutionary Computation.

[11]  Baikunth Nath,et al.  Forecasting Electricity Demand in Victoria Using Neural Networks and Statistical Methods , 1998 .

[12]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[13]  John R. Koza,et al.  Genetic Programming III: Darwinian Invention & Problem Solving , 1999 .

[14]  Martin Fodslette Meiller.  A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning , 1993 .

[15]  N. Kasabov,et al.  Rule insertion and rule extraction from evolving fuzzy neural networks: algorithms and applications for building adaptive, intelligent expert systems , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).