GRU Wave Energy Prediction Model with Bayesian Optimization
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Solution Overview
Problem
Current wave energy prediction methods are inefficient due to their complexity, long processing times, and inability to provide accurate and rapid predictions, which hinders the effective deployment and operation of wave energy converters.
Innovation Solution
A GRU-based wave energy prediction method is developed, incorporating Bayesian optimization to optimize hyperparameters and an attention mechanism to assign weights to features, allowing for improved prediction accuracy by first predicting wave height and period, then using a conversion formula to predict wave energy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If numerical models (WAM, SWAN, WW3) are used for wave parameter prediction, then prediction accuracy is improved, but processing time and implementation complexity increase significantly
Solution Approach 1:
The patent replaces complex numerical mechanical models (WAM, SWAN, WW3) with a machine learning-based prediction system that uses historical wave data and meteorological data to directly predict wave parameters, eliminating the need for computationally intensive numerical simulations while maintaining prediction accuracy
Solution Approach 2:
The patent creates a simplified predictive model that copies the essential prediction functionality from complex numerical models but implements it through machine learning algorithms that require minimal computational resources and processing time
2Measurement precision
If numerical models are used for wave prediction, then prediction accuracy is improved, but device complexity and implementation difficulty increase
Solution Approach 1:
The patent substitutes complex numerical modeling systems with a machine learning prediction system that requires simpler implementation, using standard data processing and model training procedures rather than complex numerical simulations
Solution Approach 2:
The patent creates a universal prediction model that can handle multiple wave parameters and different sea conditions through a single machine learning framework, reducing implementation complexity compared to specialized numerical models for each scenario
3Ease of manufacture
If traditional prediction methods are used, then implementation is simpler, but prediction accuracy and speed are insufficient for effective WEC deployment
Solution Approach 1:
The patent performs preliminary training of machine learning models using historical data before actual prediction operations, so that during deployment, predictions can be made rapidly without complex real-time computations, achieving both simplicity and speed
Data Source
AI summary
Method for predicting wave energy based on improved GRU A method for predicting wave energy based on improved GRU includes steps of: 1) determining input features of a prediction model; 2) using a Bayesian optimization algorithm to determine hyperparameters of the prediction model; 3) training the prediction model to obtain wave height and wave period prediction models; 4) using a test set to compare prediction results of the prediction model with observed values, so as to determine whether an optimization end condition of the Bayesian optimization algorithm is reached; and 5) using a wave energy conversion formula to convert predicted values of the wave height and the wave period into a predicted value of wave energy. The present invention improves on the original Gated Recurrent Unit (GRU) network, and proposes a GRU wave energy prediction model based on Bayesian optimization and attention mechanism.