A Linear Genetic Programming Approach for Modelling Electricity Demand Prediction in Victoria
by
Maumita Bhattacharya
,
Ajith Abraham
,
Baikunth Nath
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@MISC{Bhattacharya_alinear,
author = {Maumita Bhattacharya and Ajith Abraham and Baikunth Nath},
title = {A Linear Genetic Programming Approach for Modelling Electricity Demand Prediction in Victoria},
year = {}
}
author = {Maumita Bhattacharya and Ajith Abraham and Baikunth Nath},
title = {A Linear Genetic Programming Approach for Modelling Electricity Demand Prediction in Victoria},
year = {}
}
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Abstract:
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
