NDLI: A Hybrid Feature Selection and Generation Algorithm for Electricity Load Prediction Using Grammatical Evolution

Content Provider	IEEE Xplore Digital Library
Author	De Silva, A.M. Noorian, F. Davis, R.I.A. Leong, P.H.W.
Copyright Year	2013
Description	Author affiliation: Sch. of Electr. & Inf. Eng., Univ. of Sydney, Sydney, NSW, Australia (De Silva, A.M.; Noorian, F.; Davis, R.I.A.; Leong, P.H.W.)
Abstract	Accurate load prediction plays a major role in devising effective power system control strategies. Successful prediction systems often use machine learning (ML) methods. The success of ML methods, among other things, depends on a suitable choice of input features which are usually selected by domain-experts. In this paper, we propose a novel systematic way of generating and selecting better features for daily peak electricity load prediction using kernel methods. Grammatical evolution is used to evolve an initial population of well performing individuals, which are subsequently mapped to feature subsets derived from wavelets and technical indicator type formulae used in finance. It is shown that the generated features can improve results, while requiring no domain-specific knowledge. The proposed method is focused on feature generation and can be applied to a wide range of ML architectures and applications.
Sponsorship	IEEE Syst., Man, Cybern. Soc.
Starting Page	211
Ending Page	217
File Size	440522
Page Count	7
File Format	PDF
ISBN	9780769551449
DOI	10.1109/ICMLA.2013.125
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Reactive power grammatical evolution Electricity Sociology Production Grammar machine learning Load prediction Statistics Biological cells feature selection context-free grammar
Content Type	Text
Resource Type	Article

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