Summary of Ai-powered Digital Twin Of the Ocean: Reliable Uncertainty Quantification For Real-time Wave Height Prediction with Deep Ensemble, by Dongeon Lee et al.
AI-powered Digital Twin of the Ocean: Reliable Uncertainty Quantification for Real-time Wave Height Prediction with Deep Ensemble
by Dongeon Lee, Sunwoong Yang, Jae-Won Oh, Su-Gil Cho, Sanghyuk Kim, Namwoo Kang
First submitted to arxiv on: 7 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Signal Processing (eess.SP); Atmospheric and Oceanic Physics (physics.ao-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning model that integrates long short-term memory (LSTM) networks and deep ensemble (DE) is proposed for reliable real-time wave height prediction in wave energy converters (WECs). The LSTM-DE model leverages temporal and robust uncertainty quantification to achieve high accuracy and reliability. Real operational data from an oscillating water column-wave energy converter (OWC-WEC) system is used to evaluate the model, which achieves notable accuracy (R2 > 0.9) and increases uncertainty quality by over 50% through simple calibration technique. A comprehensive parametric study is conducted to explore the effects of key model hyperparameters, offering valuable guidelines for diverse operational scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new AI-powered tool helps predict ocean wave heights in real-time, making it easier to generate clean energy from waves. The tool uses special algorithms called LSTM and DE to make accurate predictions. It works by studying patterns in past wave data and then using that information to forecast future wave heights. The tool was tested with real data from a wave energy converter in South Korea and showed impressive accuracy, with a reliability increase of over 50% through simple adjustments. |
Keywords
* Artificial intelligence * Lstm * Machine learning
