You are not logged in.

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.

Attached Files
Name Description MIMEType Size Downloads

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
1879-3517
Keyword(s) Aggregation algorithm
Electricity load demand
Forecasting
Neural network ensemble
Science & Technology
Technology
Engineering, Electrical & Electronic
Engineering
SHORT-TERM LOAD
PREDICTION INTERVALS
CLASSIFIER FUSION
MODEL
COMBINATION
ENSEMBLES
ACCURACY
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
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 11 times in TR Web of Science
Scopus Citation Count Cited 11 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 121 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Wed, 26 Aug 2015, 09:51:07 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.