An Intelligent Control System for a Dual-Loop Solar Installation with Thermosiphon Circulation Based on Artificial Intelligence Methods

Abstract

This article explores an intelligent control system for a dual-loop solar installation with thermosiphon circulation, designed to improve the efficiency of hot water supply and heating. The proposed system integrates a flat-plate solar collector, a heat exchange loop, a heat pump, a storage tank, circulation pumps, and an AI forecasting module. The main idea of the work is to transition from traditional threshold control to adaptive control based on forecasting the temperature of the collector, the heat transfer fluid, the storage tank, the heat load, and energy consumption. To evaluate the effectiveness of the proposed approach, calculation data were generated for solar radiation, ambient temperature, system temperature conditions, pump operation, thermal efficiency, and energy consumption. The results show that AI control enables the required storage tank temperature of 50 °C to be reached more quickly, reduces the average temperature deviation from 4.2 to 2.6 °C, and increases the average system efficiency from 54.3% to 60.1%. Daily energy consumption decreases from 12.0 to 9.93 kW·h, corresponding to a 17.3% savings, while useful heat output increases from 40.2 to 44.5 kW·h/day. In addition, the number of pump switching cycles decreases from 28 to 17, which reduces the load on the equipment and improves operational reliability. The accuracy of the AI model is confirmed by the MAE, RMSE, MAPE, and R2 metrics. The highest accuracy was achieved for predicting the storage tank temperature, where MAE was 0.96 °C, RMSE was 1.27 °C, MAPE was 2.4%, and R2 reached 0.97. The results confirm that the use of artificial intelligence improves energy efficiency, temperature stability, and operational reliability of a dual-loop solar system with thermosiphon circulation.

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