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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Gang Li & Bao-Jian Li & Xu-Guang Yu & Chun-Tian Cheng, 2015. "Echo State Network with Bayesian Regularization for Forecasting Short-Term Power Production of Small Hydropower Plants," Energies, MDPI, vol. 8(10), pages 1-14, October.
  • Handle: RePEc:gam:jeners:v:8:y:2015:i:10:p:12228-12241:d:57836
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    References listed on IDEAS

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    1. Monteiro, Claudio & Ramirez-Rosado, Ignacio J. & Fernandez-Jimenez, L. Alfredo, 2013. "Short-term forecasting model for electric power production of small-hydro power plants," Renewable Energy, Elsevier, vol. 50(C), pages 387-394.
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    3. Abbasi, Tasneem & Abbasi, S.A., 2011. "Small hydro and the environmental implications of its extensive utilization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 2134-2143, May.
    4. Deihimi, Ali & Orang, Omid & Showkati, Hemen, 2013. "Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction," Energy, Elsevier, vol. 57(C), pages 382-401.
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    Cited by:

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    2. Yuzgec, Ugur & Dokur, Emrah & Balci, Mehmet, 2024. "A novel hybrid model based on Empirical Mode Decomposition and Echo State Network for wind power forecasting," Energy, Elsevier, vol. 300(C).
    3. Fan Yang & Yongan Wang & Manling Dong & Xiaokuo Kou & Degui Yao & Xing Li & Bing Gao & Irfan Ullah, 2017. "A Cycle Voltage Measurement Method and Application in Grounding Grids Fault Location," Energies, MDPI, vol. 10(11), pages 1-18, November.
    4. Lintao Yang & Honggeng Yang, 2019. "Analysis of Different Neural Networks and a New Architecture for Short-Term Load Forecasting," Energies, MDPI, vol. 12(8), pages 1-23, April.
    5. Gabriel Trierweiler Ribeiro & João Guilherme Sauer & Naylene Fraccanabbia & Viviana Cocco Mariani & Leandro dos Santos Coelho, 2020. "Bayesian Optimized Echo State Network Applied to Short-Term Load Forecasting," Energies, MDPI, vol. 13(9), pages 1-19, May.

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