Deep Learning-Driven Forecasting for Compressed Air Oxygenation Integrating With Floating PV Power Generation System

Abstract

Insufficient dissolved oxygen in aquaculture systems poses a significant challenge to sustainable fish farming, while traditional aeration systems rely heavily on grid electricity, contributing to both operational costs and environmental impact. This study addresses these challenges by integrating a compressed air oxygenation system with floating solar photovoltaic (PV) power generation, supported by deep learning-based forecasting for optimal system control. Our key contributions include: (1) development of an integrated floating PV-powered compressed air oxygenation system for aquaculture, (2) implementation and comparative analysis of three deep learning models (RNN, GRU and LSTM) for forecasting both PV power generation and compressed air production and (3) validation through a real-world case study in Thailand's Pathum Thani Province. The LSTM model demonstrated superior performance, achieving the highest accuracy with RMSE of 172.59 kW and MAPE of 13.87% for PV power forecasting, and a MAPE of 21.72% for compressed air production forecasting. The implemented system successfully improved water quality in a 1200-cubic-metre freshwater fish pond, increasing dissolved oxygen levels from 1.7 to 6.47 mg/L over a 4-month period. These results demonstrate the feasibility and effectiveness of renewable energy integration in aquaculture water treatment, offering a sustainable solution for fish farming operations while reducing dependency on grid electricity.