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Ensemble empirical mode decomposition and long short-term memory neural network for multi-step predictions of time series signals in nuclear power plants

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  • Nguyen, Hoang-Phuong
  • Baraldi, Piero
  • Zio, Enrico

Abstract

We address the problem of multi-step ahead time series signal prediction in the energy industry, with the aim of improving maintenance planning and minimizing unexpected shutdowns. For this, we develop a novel method based on the combined use of Ensemble Empirical Mode Decomposition and Long Short-Term Memory neural network. Ensemble Empirical Mode Decomposition decomposes the time series into a set of Intrinsic Mode Function components which facilitate the prediction task by effectively describing the system dynamics. Then, Long Short-Term Memory neural network models perform the multi-step ahead prediction of the individual Ensemble Empirical Mode Decomposition components and the obtained predictions are aggregated to reconstruct the time series. A Tree-structured Parzen Estimator algorithm is employed for the optimization of the hyperparameters of the Long Short-Term Memory neural network. The proposed method is validated by considering various long-term prediction horizons of real time series data acquired from Reactor Coolant Pumps of Nuclear Power Plants. The results show the superior performance of the proposed method with respect to alternative state of the art methods.

Suggested Citation

  • Nguyen, Hoang-Phuong & Baraldi, Piero & Zio, Enrico, 2021. "Ensemble empirical mode decomposition and long short-term memory neural network for multi-step predictions of time series signals in nuclear power plants," Applied Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:appene:v:283:y:2021:i:c:s0306261920317281
    DOI: 10.1016/j.apenergy.2020.116346
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    6. Yang, Kailing & Zhang, Xi & Luo, Haojia & Hou, Xianping & Lin, Yu & Wu, Jingyu & Yu, Liang, 2024. "Predicting energy prices based on a novel hybrid machine learning: Comprehensive study of multi-step price forecasting," Energy, Elsevier, vol. 298(C).
    7. Hamid Nasiri & Mohammad Mehdi Ebadzadeh, 2022. "Multi-step-ahead Stock Price Prediction Using Recurrent Fuzzy Neural Network and Variational Mode Decomposition," Papers 2212.14687, arXiv.org.
    8. Junfeng Wu & Hanyu Chen & Xu Li & Guohua Kang & Yuangang Lu, 2022. "Correlation coefficient local capping REMD adaptive filtering method for laser interference signal," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-12, January.
    9. Qiao, Weibiao & Fu, Zonghua & Du, Mingjun & Nan, Wei & Liu, Enbin, 2023. "Seasonal peak load prediction of underground gas storage using a novel two-stage model combining improved complete ensemble empirical mode decomposition and long short-term memory with a sparrow searc," Energy, Elsevier, vol. 274(C).
    10. Parvaiz Ahmad Ahangar & Shameem Ahmad Lone & Neeraj Gupta, 2023. "Combining Data-Driven and Model-Driven Approaches for Optimal Distributed Control of Standalone Microgrid," Sustainability, MDPI, vol. 15(16), pages 1-19, August.
    11. Zizhen Cheng & Li Wang & Yumeng Yang, 2023. "A Hybrid Feature Pyramid CNN-LSTM Model with Seasonal Inflection Month Correction for Medium- and Long-Term Power Load Forecasting," Energies, MDPI, vol. 16(7), pages 1-18, March.
    12. Zhao, Yuan & Zhang, Weiguo & Gong, Xue & Wang, Chao, 2021. "A novel method for online real-time forecasting of crude oil price," Applied Energy, Elsevier, vol. 303(C).
    13. Pinciroli, Luca & Baraldi, Piero & Zio, Enrico, 2023. "Maintenance optimization in industry 4.0," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    14. Wang, Chu & Dou, Manfeng & Li, Zhongliang & Outbib, Rachid & Zhao, Dongdong & Zuo, Jian & Wang, Yuanlin & Liang, Bin & Wang, Peng, 2023. "Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    15. Jia, Xiongjie & Sang, Yichen & Li, Yanjun & Du, Wei & Zhang, Guolei, 2022. "Short-term forecasting for supercharged boiler safety performance based on advanced data-driven modelling framework," Energy, Elsevier, vol. 239(PE).

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