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

Gang Li, Bao-Jian Li, Xu-Guang Yu and Chun-Tian Cheng
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Gang Li: Institute of Hydropower System and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Bao-Jian Li: Institute of Hydropower System and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Xu-Guang Yu: Institute of Hydropower System and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Chun-Tian Cheng: Institute of Hydropower System and Hydroinformatics, Dalian University of Technology, Dalian 116024, China

Energies, 2015, vol. 8, issue 10, 1-14

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.

Keywords: SHP; power production forecasting; echo state network; Bayesian regularization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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