Examining performance of aggregation algorithms for neural network-based electricity demand forecasting

Hassan, Saima, Khosravi, Abbas and Jaafar, Jafreezal 2015, Examining performance of aggregation algorithms for neural network-based electricity demand forecasting, International journal of electrical power and energy systems, vol. 64, pp. 1098-1105, doi: 10.1016/j.ijepes.2014.08.025.

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Title Examining performance of aggregation algorithms for neural network-based electricity demand forecasting
Author(s) Hassan, Saima
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Jaafar, Jafreezal
Journal name International journal of electrical power and energy systems
Volume number 64
Start page 1098
End page 1105
Total pages 8
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015
ISSN 0142-0615
Keyword(s) Aggregation algorithm
Electricity load demand
Neural network ensemble
Science & Technology
Engineering, Electrical & Electronic
Summary The aim of this research is to examine the efficiency of different aggregation algorithms to the forecasts obtained from individual neural network (NN) models in an ensemble. In this study an ensemble of 100 NN models are constructed with a heterogeneous architecture. The outputs from NN models are combined by three different aggregation algorithms. These aggregation algorithms comprise of a simple average, trimmed mean, and a Bayesian model averaging. These methods are utilized with certain modifications and are employed on the forecasts obtained from all individual NN models. The output of the aggregation algorithms is analyzed and compared with the individual NN models used in NN ensemble and with a Naive approach. Thirty-minutes interval electricity demand data from Australian Energy Market Operator (AEMO) and the New York Independent System Operator's web site (NYISO) are used in the empirical analysis. It is observed that the aggregation algorithm perform better than many of the individual NN models. In comparison with the Naive approach, the aggregation algorithms exhibit somewhat better forecasting performance.
Language eng
DOI 10.1016/j.ijepes.2014.08.025
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30075811

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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