Nonlinear maximum likelihood estimation of electricity spot prices using recurrent neural networks

Authors:
Derrick Mirikitani;Nikolay Nikolaev
Affiliations:
Goldsmiths College, Department of Computing, SE14 6NW, New Cross, London, UK;Goldsmiths College, Department of Computing, SE14 6NW, New Cross, London, UK
Venue:
Neural Computing and Applications
Year:
2011

Citing 0
Cited 2

Discrete-time inverse optimal neural control for synchronous generators

Engineering Applications of Artificial Intelligence
A hybrid intelligent model based on recurrent neural networks and excitable dynamics for price prediction in deregulated electricity market

Engineering Applications of Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Electricity spot prices are complex processes characterized by nonlinearity and extreme volatility. Previous work on nonlinear modeling of electricity spot prices has shown encouraging results, and we build on this area by proposing an Expectation Maximization algorithm for maximum likelihood estimation of recurrent neural networks utilizing the Kalman filter and smoother. This involves inference of both parameters and hyper-parameters of the model which takes into account the model uncertainty and noise in the data. The Expectation Maximization algorithm uses a forward filtering and backward smoothing (Expectation) step, followed by a hyper-parameter estimation (Maximization) step. The model is validated across two data sets of different power exchanges. It is found that after learning a posteriori hyper-parameters, the proposed algorithm outperforms the real-time recurrent learning and the extended Kalman Filtering algorithm for recurrent networks, as well as other contemporary models that have been previously applied to the modeling of electricity spot prices.