Wave Energy Control for Offshore Aquaculture Load Balancing
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Solution Overview
Problem
In offshore aquaculture, wave energy yield volatility leads to resource waste during high yields, resource shortages during low yields, and low apparatus operation efficiency due to fixed operation parameters.
Innovation Solution
A method and system using a neural network model with LSTM and GRU layers to predict wave energy yield, combined with RFE for importance coefficient sorting, adjusts operation cycles and power of different aquaculture apparatus types based on zone location and importance coefficients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed operation parameters are used for aquaculture apparatus, then operation simplicity is maintained, but apparatus operation efficiency decreases during periods of wave energy yield volatility
Solution Approach 1:
The patent implements dynamic adjustment of apparatus operation parameters based on real-time wave energy yield predictions. The system continuously adapts operation cycles and power levels according to predicted energy availability, transforming fixed static parameters into flexible dynamic parameters that respond to changing conditions, thereby resolving the contradiction between operational simplicity and efficiency.
Solution Approach 2:
The system establishes a feedback loop where wave energy yield predictions inform operation parameter adjustments. By continuously monitoring predicted energy yields and adjusting apparatus operations accordingly, the system creates a closed-loop control mechanism that maintains high efficiency while preserving operational simplicity through automated decision-making.
2Loss of energy
If wave energy is fully utilized when yield is high, then energy resource utilization improves, but resource waste occurs when yield exceeds demand
Solution Approach 1:
The system implements partial utilization of wave energy by adjusting apparatus operation parameters to match actual energy demand. When wave energy yield exceeds demand, the system reduces operation cycles or power levels of non-critical apparatus, deliberately using only the necessary portion of available energy rather than fully utilizing all available energy, thereby preventing waste while maintaining efficient resource use.
Solution Approach 2:
The system changes operational parameters such as operation cycles and power levels based on the relationship between wave energy yield and demand. By dynamically adjusting these parameters, the system optimizes energy utilization to match actual needs, preventing both waste from excessive utilization and shortage from insufficient utilization.
3Stability of the object's composition
If apparatus operates in fixed manner during low wave energy yield, then operation stability is maintained, but power supply becomes insufficient affecting normal operation
Solution Approach 1:
The system dynamically adjusts operation parameters based on predicted wave energy yield levels. During low energy yield periods, the system automatically reduces power consumption of non-critical apparatus while maintaining stable operation of essential functions, transforming fixed operation modes into adaptive dynamic modes that ensure both stability and reliability.
Solution Approach 2:
The system applies different operation strategies to different apparatus or different functions based on their importance. During low energy yield, critical apparatus maintain stable operation while non-critical apparatus reduce power consumption, creating localized quality differences in operation intensity that preserve overall system reliability and stability simultaneously.
Data Source
AI summary
Disclosed in the present disclosure is a method and system for controlling and distributing wave energy in offshore aquaculture. The method includes: obtaining an aquaculture cycle of each aquaculture sub-zone of an offshore aquaculture zone, sorting remaining aquaculture cycles of the aquaculture sub-zones from small to large, and obtaining a plurality of work cycles according to sorting results; obtaining a predicted wave energy yield of a next work cycle through a preset neural network model; obtaining an importance coefficient value sorting result of each aquaculture zone through a preset recursive feature elimination (RFE) model; and adjusting operation cycles and operation power of first-type aquaculture apparatuses, second-type aquaculture apparatuses, and third-type aquaculture apparatuses in sequence according to an apparatus type of each aquaculture apparatus, the aquaculture zone where each aquaculture apparatus is located, the predicted wave energy yield, and the importance coefficient value sorting results.