Abstract
Accurate electricity price forecasting is critical to market participants in wholesale electricity markets. The problem becomes more complex because the acquired data series are non-linear and non-Gaussian. In this paper, Multi Layer Perceptrons (MLP) trained with minimizing error entropy (MEE) algorithm is utilized to forecast electricity price. Compared with the conventional MLP with mean square error (MSE) criterion, the proposed approach can achieve better performance in simulated examples.
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Zhang, J., Wang, J., Wang, R., Hou, G. (2010). Electricity Price Forecasting Using Neural Networks Trained with Entropic Criterion. In: Zeng, Z., Wang, J. (eds) Advances in Neural Network Research and Applications. Lecture Notes in Electrical Engineering, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12990-2_6
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DOI: https://doi.org/10.1007/978-3-642-12990-2_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12989-6
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