- title = "A Linear Genetic Programming Approach for Modeling Electricity Demand Prediction in Victoria",
- author = "Maumita Bhattacharya and Ajith Abraham and Baikunth Nath",
- editor = "Ajith Abraham and Mario Koppen",
- booktitle = "2001 International Workshop on Hybrid Intelligent Systems",
- series = "LNCS",
- pages = "379--394",
- publisher = "Springer-Verlag",
- address = "Adelaide, Australia",
- publisher_address = "Berlin",
- month = "11-12 " # dec,
- year = "2001",
- email = "maumita.bhattacharya@infotech.monash.edu.au, ajith.abraham@infotech.monash.edu.au, b.nath@infotech.monash.edu.au",
- keywords = "genetic algorithms, genetic programming, Linear genetic programming, neuro-fuzzy, neural networks, forecasting, electricity demand",
- broken = "http://www-mugc.cc.monash.edu.au/~abrahamp/172.pdf",
-
URL = "
http://www.springer.de/cgi-bin/search_book.pl?isbn=3-7908-1480-6",
-
URL = "http://citeseer.ist.psu.edu/510872.html",
- ISBN = "3-7908-1480-6",
- 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.",
-
notes = "HIS01
Possibly also of interest Applied Soft Computing Volume 1, Issue 2 , August 2001, Pages 127-138 doi:10.1016/S1568-4946(01)00013-8",
