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

2024-12-07

AI Summary

Low energy conversion efficiency in wave energy conversion—caused by highly variable wave heights—and unreliable uncertainty quantification (UQ) in existing predictive models hinder the operational reliability of oscillating water column wave energy converters (OWC-WECs). Method: This study develops an AI-driven digital twin system tailored for OWC-WECs, featuring a novel hybrid architecture integrating Long Short-Term Memory (LSTM) networks with Deep Ensembles (DE), augmented by uncertainty calibration and a multi-scenario hyperparameter sensitivity analysis framework. Contribution/Results: The system significantly improves prediction accuracy and model generalizability while enhancing interpretability. Validated on real-world measurement data from Jeju Island, it achieves an R² > 0.9 and improves UQ quality by over 50%, demonstrating robustness and engineering applicability in highly dynamic marine environments.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Unstable wave size
• Low energy efficiency
• Accuracy and reliability of wave prediction

Innovation

• Advanced AI
• Wave Height Prediction
• Uncertainty Quantification