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Electricity demand modelling with genetic programming

TitleElectricity demand modelling with genetic programming
Publication TypeArticolo su Rivista peer-reviewed
Year of Publication2015
AuthorsCastelli, M., De Felice Matteo, Manzoni L., Vanneschi L., Machado P., Costa E., and Cardoso A.
Secondary AuthorsF., Pereira
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9273
Pagination213-225
ISBN Number9783319234847
ISSN03029743
KeywordsAir conditioning, Artificial intelligence, Computer systems programming, Critical tasks, Electric grids, Electric power plant loads, Electricity demands, Energy demands, Genetic algorithms, Genetic programming, Human being, Load forecasting, Model trees, Neural networks ensembles
Abstract

Load forecasting is a critical task for all the operations of power systems. Especially during hot seasons, the influence of weather on energy demand may be strong, principally due to the use of air conditioning and refrigeration. This paper investigates the application of Genetic Programming on day-ahead load forecasting, comparing it with Neural Networks, Neural Networks Ensembles and Model Trees. All the experimentations have been performed on real data collected from the Italian electric grid during the summer period. Results show the suitability of Genetic Programming in providing good solutions to this problem. The advantage of using Genetic Programming, with respect to the other methods, is its ability to produce solutions that explain data in an intuitively meaningful way and that could be easily interpreted by a human being. This fact allows the practitioner to gain a better understanding of the problem under exam and to analyze the interactions between the features that characterize it. © Springer International Publishing Switzerland 2015.

Notes

cited By 0; Conference of 17th Portuguese Conference on Artificial Intelligence, EPIA 2015 ; Conference Date: 8 September 2015 Through 11 September 2015; Conference Code:140439

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84945922460&doi=10.1007%2f978-3-319-23485-4_22&partnerID=40&md5=c818ec1c8a5ba1f4cc2d262de657c50c
DOI10.1007/978-3-319-23485-4_22
Citation KeyCastelli2015213