Smart integrated aquaponics system: Hybrid solar-hydro energy with deep learning forecasting for optimized energy management in aquaculture and hydroponics

Abstract

The global pursuit of sustainable energy and food production has led to the creation of integrated systems that maximize efficiency and minimize environmental impact. This research introduces the Smart Integrated Aquaponics System, combining hybrid solar-hydro energy with AI-driven forecasting and IoT-based monitoring to optimize aquaponics. By harnessing renewable energy and artificial intelligence, the system addresses challenges such as energy variability, resource efficiency, and scalability, particularly relevant to urban farming in land-scarce regions like Indonesia. The system integrates photovoltaic (PV) and micro-hydro sources with a hybrid energy management system for uninterrupted power. A long short-term memory recurrent neural network (LSTM-RNN) ensures precise energy forecasting, achieving mean absolute errors of 0.0579 for voltage and 0.1109 for power output. IoT sensors and convolutional neural networks (CNNs) monitor fish health and plant growth, providing accurate resource management and scalability. Experimental results highlight its effectiveness: solar irradiance peaked at 1200 W/m², while micro-hydro turbines maintained stable power. Water treatment reduced turbidity below 10 NTU and total dissolved solids to 50 ppm, ensuring optimal water quality. Fish growth classification confidence ranged from 0.92 to 0.95, while plant monitoring accurately tracked development. Challenges remain, including seasonal energy variability and scalability. Enhancing energy storage, improving forecasting, and streamlining integration can address these issues. This research sets a benchmark for sustainable agriculture by demonstrating how hybrid energy systems, AI, and IoT can create scalable, efficient, and eco-friendly solutions, advancing global food and energy security.