TY - GEN
T1 - Electricity demand modelling with genetic programming
AU - Castelli, Mauro
AU - De Felice, Matteo
AU - Manzoni, Luca
AU - Vanneschi, Leonardo
N1 - Castelli, M., De Felice, M., Manzoni, L., & Vanneschi, L. (2015). Electricity demand modelling with genetic programming. In Progress in Artificial Intelligence - 17th Portuguese Conference on Artificial Intelligence, EPIA 2015, Proceedings (Vol. 9273, pp. 213-225). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9273). Springer-Verlag. https://doi.org/10.1007/978-3-319-23485-4_22
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84945922460&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-23485-4_22
DO - 10.1007/978-3-319-23485-4_22
M3 - Conference contribution
AN - SCOPUS:84945922460
SN - 9783319234847
VL - 9273
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 213
EP - 225
BT - Progress in Artificial Intelligence - 17th Portuguese Conference on Artificial Intelligence, EPIA 2015, Proceedings
PB - Springer-Verlag
T2 - 17th Portuguese Conference on Artificial Intelligence, EPIA 2015
Y2 - 8 September 2015 through 11 September 2015
ER -