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AI-powered Digital Twin of the Ocean: Reliable Uncertainty Quantification for Real-time Wave Height Prediction with Deep Ensemble

Dongeon Lee, Sunwoong Yang, Jae-Won Oh, Su-Gil Cho, Sanghyuk Kim, Namwoo Kang

TL;DR

An AI-powered reliable real-time wave height prediction model that integrates long short-term memory (LSTM) networks for temporal prediction with deep ensemble (DE) for robust uncertainty quantification (UQ) for robust uncertainty quantification (UQ), ensuring high accuracy and reliability is proposed.

Abstract

Environmental pollution and fossil fuel depletion have prompted the need for renewable energy-based power generation. However, its stability is often challenged by low energy density and non-stationary conditions. Wave energy converters (WECs), in particular, need reliable real-time wave height prediction to address these issues caused by irregular wave patterns, which can lead to the inefficient and unstable operation of WECs. In this study, we propose an AI-powered reliable real-time wave height prediction model that integrates long short-term memory (LSTM) networks for temporal prediction with deep ensemble (DE) for robust uncertainty quantification (UQ), ensuring high accuracy and reliability. To further enhance the reliability, uncertainty calibration is applied, which has proven to significantly improve the quality of the quantified uncertainty. Using real operational data from an oscillating water column-wave energy converter (OWC-WEC) system in Jeju, South Korea, the model achieves notable accuracy (R2 > 0.9), while increasing uncertainty quality by over 50% through simple calibration technique. Furthermore, a comprehensive parametric study is conducted to explore the effects of key model hyperparameters, offering valuable guidelines for diverse operational scenarios, characterized by differences in wavelength, amplitude, and period. These results demonstrate the model's capability to deliver reliable predictions, facilitating digital twin of the ocean.

AI-powered Digital Twin of the Ocean: Reliable Uncertainty Quantification for Real-time Wave Height Prediction with Deep Ensemble

TL;DR

An AI-powered reliable real-time wave height prediction model that integrates long short-term memory (LSTM) networks for temporal prediction with deep ensemble (DE) for robust uncertainty quantification (UQ) for robust uncertainty quantification (UQ), ensuring high accuracy and reliability is proposed.

Abstract

Environmental pollution and fossil fuel depletion have prompted the need for renewable energy-based power generation. However, its stability is often challenged by low energy density and non-stationary conditions. Wave energy converters (WECs), in particular, need reliable real-time wave height prediction to address these issues caused by irregular wave patterns, which can lead to the inefficient and unstable operation of WECs. In this study, we propose an AI-powered reliable real-time wave height prediction model that integrates long short-term memory (LSTM) networks for temporal prediction with deep ensemble (DE) for robust uncertainty quantification (UQ), ensuring high accuracy and reliability. To further enhance the reliability, uncertainty calibration is applied, which has proven to significantly improve the quality of the quantified uncertainty. Using real operational data from an oscillating water column-wave energy converter (OWC-WEC) system in Jeju, South Korea, the model achieves notable accuracy (R2 > 0.9), while increasing uncertainty quality by over 50% through simple calibration technique. Furthermore, a comprehensive parametric study is conducted to explore the effects of key model hyperparameters, offering valuable guidelines for diverse operational scenarios, characterized by differences in wavelength, amplitude, and period. These results demonstrate the model's capability to deliver reliable predictions, facilitating digital twin of the ocean.

Paper Structure

This paper contains 27 sections, 8 equations, 13 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Oscillating water column-wave energy converter (OWC-WEC)
  4. Long short-term memory (LSTM) network for temporal prediction
  5. Deep ensemble (DE) for UQ
  6. Evaluation metrics
  7. Prediction performance
  8. Root mean squared error (RMSE)
  9. Mean absolute percentage error (MAPE)
  10. Coefficient of determination ($R^2$)
  11. Quality of UQ
  12. Area under the calibration error (AUCE)
  13. Standard deviation (STD) scaling for uncertainty calibration
  14. Proposed method: LSTM-DE
  15. Necessity of the proposed method
  16. ...and 12 more sections

Figures (13)

  • Figure 1: 500kW OWC-WEC at Yongsoo, Jeju, South Korea.
  • Figure 2: Digital twin of OWC-WEC developed by KRISO.
  • Figure 3: Schematic diagram of OWC-WEC.
  • Figure 4: Architecture of LSTM cell.
  • Figure 5: Architecture of DE approach.
  • ...and 8 more figures