Electricity demand modelling with genetic programming

Mauro Castelli, Matteo De Felice, Luca Manzoni, Leonardo Vanneschi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationProgress in Artificial Intelligence - 17th Portuguese Conference on Artificial Intelligence, EPIA 2015, Proceedings
Number of pages13
ISBN (Print)9783319234847
Publication statusPublished - 2015
Event17th Portuguese Conference on Artificial Intelligence, EPIA 2015 - Coimbra, Portugal
Duration: 8 Sep 201511 Sep 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)03029743
ISSN (Electronic)16113349


Conference17th Portuguese Conference on Artificial Intelligence, EPIA 2015


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