Hybrid neural-evolutionary model for electricity price forecasting Srinivasan, Dipti, Guofan, Zhang, Khosravi, Abbas, Nahavandi, Saeid and Creighton, Doug 2011, Hybrid neural-evolutionary model for electricity price forecasting, in IJCNN 2011 : Proceedings of the International Joint Conference on Neural Networks, IEEE, [San Jose, Calif.], pp. 3164-3169. Attached Files Name Description MIMEType Size Downloads Title Hybrid neural-evolutionary model for electricity price forecasting Author(s) Srinivasan, Dipti Guofan, Zhang Khosravi, Abbas orcid.org/0000-0001-6927-0744 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Creighton, Doug orcid.org/0000-0002-9217-1231 Conference name International Joint Conference on Neural Network (2011 : San Jose, Calif.) Conference location San Jose, Calif. Conference dates 31 July-5 Aug. 2011 Title of proceedings IJCNN 2011 : Proceedings of the International Joint Conference on Neural Networks Editor(s) [Unknown] Publication date 2011 Conference series International Joint Conference on Neural Network Start page 3164 End page 3169 Total pages 6 Publisher IEEE Place of publication [San Jose, Calif.] Keyword(s) electricity price forecasting models hybrid algorithms neural network Summary Evolving artificial neural networks has attracted much attention among researchers recently, especially in the fields where plenty of data exist but explanatory theories and models are lacking or based upon too many simplifying assumptions. Financial time series forecasting is one of them. A hybrid model is used to forecast the hourly electricity price from the California Power Exchange. A collaborative approach is adopted to combine ANN and evolutionary algorithm. The main contributions of this thesis include: Investigated the effect of changing values of several important parameters on the performance of the model, and selected the best combination of these parameters; good forecasting results have been obtained with the implemented hybrid model when the best combination of parameters is used. The lowest MAPE through a single run is 5. 28134%. And the lowest averaged MAPE over 10 runs is 6.088%, over 30 runs is 6.786%; through the investigation of the parameter period, it is found that by including future values of the homogenous moments of the instant being forecasted into the input vector, forecasting accuracy is greatly enhanced. A comparison of results with other works reported in the literature shows that the proposed model gives superior performance on the same data set. ISBN 9781457710865 Language eng Field of Research 089999 Information and Computing Sciences not elsewhere classified Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1 Full written paper - refereed Copyright notice ©2011, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30042228 Document type: Conference Paper Collections: Institute for Intelligent Systems Research and Innovation (IISRI) 2018 ERA Submission GTP Research Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 14 Feb 2012, 15:20:34 EST Thu, 15 Mar 2012, 08:02:51 EST Thu, 15 Mar 2012, 08:05:16 EST Fri, 30 Mar 2012, 08:15:32 EST Fri, 30 Mar 2012, 08:16:10 EST Tue, 03 Apr 2012, 08:44:33 EST Tue, 03 Apr 2012, 08:45:44 EST Tue, 28 Jan 2014, 11:58:37 EST Wed, 02 Jul 2014, 15:36:53 EST Fri, 17 Oct 2014, 11:17:34 EST Sat, 15 Apr 2017, 02:33:30 EST Filtered Full Citation counts:   Cited 6 times in TR Web of Science Cited 7 times in Scopus Search Google Scholar Access Statistics: 647 Abstract Views, 12 File Downloads  -  Detailed Statistics Created: Tue, 14 Feb 2012, 15:20:34 EST

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