Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.engappai.2012.12.012
Title: A hybrid intelligent model based on recurrent neural networks and excitable dynamics for price prediction in deregulated electricity market
Authors: Sharma, V.
Srinivasan, D. 
Keywords: Excitable system
FHN coupled system
Multiple scale dynamics
Recurrent neural networks
Issue Date: May-2013
Source: Sharma, V., Srinivasan, D. (2013-05). A hybrid intelligent model based on recurrent neural networks and excitable dynamics for price prediction in deregulated electricity market. Engineering Applications of Artificial Intelligence 26 (5-6) : 1562-1574. ScholarBank@NUS Repository. https://doi.org/10.1016/j.engappai.2012.12.012
Abstract: This paper examines electricity price time series from dynamical system perspective and proposes a hybrid model which employs a synergistic combination of Recurrent Neural Network (RNN) and coupled excitable system for prediction of future prices in deregulated electricity markets. Driven by profit maximizing decisions taken by various agents, these markets belong to the class of financial systems. However presence of intermittent spikes and complex dynamic nonlinearities in electricity price time series render the prediction task extremely challenging. The approximation ability of Recurrent Neural Networks to map dynamic functions together with sharp jumping attribute of coupled excitable systems allows close approximation of spiky time series. The developed hybrid model was applied for point and interval forecasting in various markets worldwide over different seasons for testing its adaptability in different environments. Satisfactory prediction results were obtained in all the markets, in stable as well as spiking regions of the time series. © 2013 Elsevier Ltd.
Source Title: Engineering Applications of Artificial Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/54270
ISSN: 09521976
DOI: 10.1016/j.engappai.2012.12.012
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