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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

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.
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https://hal-mines-paristech.archives-ouvertes.fr/hal-03481321
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Submitted on : Wednesday, December 15, 2021 - 11:48:18 AM
Last modification on : Monday, January 3, 2022 - 2:54:01 PM

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Hoang-Phuong Nguyen, Piero Baraldi, Enrico Zio. 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, 2021, 283, pp.116346. ⟨10.1016/j.apenergy.2020.116346⟩. ⟨hal-03481321⟩

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