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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.

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Title Hybrid neural-evolutionary model for electricity price forecasting
Author(s) Srinivasan, Dipti
Guofan, Zhang
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Creighton, DougORCID iD for 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
Collection: Centre for Intelligent Systems Research
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