Electricity Demand Modelling with Genetic Programming

被引:0
|
作者
Castelli, Mauro [1 ]
De Felice, Matteo [2 ]
Manzoni, Luca [3 ]
Vanneschi, Leonardo [1 ]
机构
[1] Univ Nova Lisboa, NOVA IMS, P-1070312 Lisbon, Portugal
[2] ENEA, Casaccia Res Ctr, Energy & Environm Modeling Tech Unit UTMEA, I-00123 Rome, Italy
[3] Univ Milano Bicocca, Dipartimento Informat Sistemist & Comunicaz, I-20126 Milan, Italy
来源
关键词
NEURAL-NETWORKS; PREDICTION;
D O I
10.1007/978-3-319-23485-4_22
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:213 / 225
页数:13
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