Improving load forecasting accuracy through combination of best forecasts

Hassan, Saima, Khosravi, Abbas and Jaafar, Jafreezal 2012, Improving load forecasting accuracy through combination of best forecasts, in POWERCON 2012 : Proceedings of the 2012 IEEE International Conference on Power System Technology, IEEE, Piscataway, N.J., pp. 1-6.

Attached Files
Name Description MIMEType Size Downloads

Title Improving load forecasting accuracy through combination of best forecasts
Author(s) Hassan, Saima
Khosravi, Abbas
Jaafar, Jafreezal
Conference name IEEE International Conference on Power System Technology (2012 : Auckland, N.Z)
Conference location Auckland, New Zealand
Conference dates 30 Oct.-2 Nov. 2012
Title of proceedings POWERCON 2012 : Proceedings of the 2012 IEEE International Conference on Power System Technology
Editor(s) [Unknown]
Publication date 2012
Conference series IEEE International Conference on Power System Technology
Start page 1
End page 6
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) forecasts combination
load demand
neural networks
short-term forecasting
Summary Neural network (NN) models have been widely used in the literature for short-term load forecasting. Their popularity is mainly due to their excellent learning and approximation capability. However, their forecasting performance significantly depends on several factors including initializing parameters, training algorithm, and NN structure. To minimize negative effects of these factors, this paper proposes a practically simple, yet effective and an efficient method to combine forecasts generated by NN models. The proposed method includes three main phases: (i) training NNs with different structures, (ii) selecting best NN models based on their forecasting performance for a validation set, and (iii) combination of forecasts for selected best NNs. Forecast combination is performed through calculating the mean of forecasts generated by best NN models. The performance of the proposed method is examined using real world data set. Comparative studies demonstrate that the accuracy of combined forecasts is significantly superior to those obtained from individual NN models.
ISBN 9781467328685
Language eng
Field of Research 080110 Simulation and Modelling
Socio Economic Objective 850699 Energy Storage, Distribution and Supply not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30052624

Document type: Conference Paper
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
Access Statistics: 38 Abstract Views, 3 File Downloads  -  Detailed Statistics
Created: Fri, 24 May 2013, 12:06:58 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.