Neural network ensemble : evaluation of aggregation algorithms for electricity demand forecasting

Hassan, Saima, Khosravi, Abbas and Jaafar, Jafreezal 2013, Neural network ensemble : evaluation of aggregation algorithms for electricity demand forecasting, in IJCNN 2013 : Proceedings of the 2013 International Joint Conference on Neural Networks, IEEE, Piscataway, N.J., pp. 2133-2138.

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Title Neural network ensemble : evaluation of aggregation algorithms for electricity demand forecasting
Author(s) Hassan, Saima
Khosravi, Abbas
Jaafar, Jafreezal
Conference name International Joint Conference on Neural Networks (2013 : Dallas, Texas)
Conference location Dallas, Texas
Conference dates 4-9 Aug. 2013
Title of proceedings IJCNN 2013 : Proceedings of the 2013 International Joint Conference on Neural Networks
Editor(s) [Unknown]
Publication date 2013
Conference series International Joint Conference on Neural Networks
Start page 2133
End page 2138
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Summary This paper examines and analyzes different aggregation algorithms to improve accuracy of forecasts obtained using neural network (NN) ensembles. These algorithms include equal-weights combination of Best NN models, combination of trimmed forecasts, and Bayesian Model Averaging (BMA). The predictive performance of these algorithms are evaluated using Australian electricity demand data. The output of the aggregation algorithms of NN ensembles are compared with a Naive approach. Mean absolute percentage error is applied as the performance index for assessing the quality of aggregated forecasts. Through comprehensive simulations, it is found that the aggregation algorithms can significantly improve the forecasting accuracies. The BMA algorithm also demonstrates the best performance amongst aggregation algorithms investigated in this study.
ISBN 9781467361286
9781467361293
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 850699 Energy Storage, Distribution and Supply not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30057171

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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