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Energies 2015, 8(10), 12228-12241; doi:10.3390/en81012228

Echo State Network with Bayesian Regularization for Forecasting Short-Term Power Production of Small Hydropower Plants

Institute of Hydropower System and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
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Author to whom correspondence should be addressed.
Academic Editor: Edwin A. Cowen
Received: 7 May 2015 / Revised: 10 October 2015 / Accepted: 16 October 2015 / Published: 27 October 2015
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Abstract

As a novel recurrent neural network (RNN), an echo state network (ESN) that utilizes a reservoir with many randomly connected internal units and only trains the readout, avoids increased complexity of training procedures faced by traditional RNN. The ESN can cope with complex nonlinear systems because of its dynamical properties and has been applied in hydrological forecasting and load forecasting. Due to the linear regression algorithm usually adopted by generic ESN to train the output weights, an ill-conditioned solution might occur, degrading the generalization ability of the ESN. In this study, the ESN with Bayesian regularization (BESN) is proposed for short-term power production forecasting of small hydropower (SHP) plants. According to the Bayesian theory, the weights distribution in space is considered and the optimal output weights are obtained by maximizing the posterior probabilistic distribution. The evidence procedure is employed to gain optimal hyperparameters for the BESN model. The recorded data obtained from the SHP plants in two different counties, located in Yunnan Province, China, are utilized to validate the proposed model. For comparison, the feed-forward neural networks with Levenberg-Marquardt algorithm (LM-FNN) and the generic ESN are also employed. The results indicate that BESN outperforms both LM-FNN and ESN. View Full-Text
Keywords: SHP; power production forecasting; echo state network; Bayesian regularization SHP; power production forecasting; echo state network; Bayesian regularization
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Li, G.; Li, B.-J.; Yu, X.-G.; Cheng, C.-T. Echo State Network with Bayesian Regularization for Forecasting Short-Term Power Production of Small Hydropower Plants. Energies 2015, 8, 12228-12241.

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