This article presents a mathematical model and analysis of the operating modes of a solar water-heating system with pump circulation. The aim of the study is to evaluate the daily dynamics of the system’s main thermal parameters based on calculated time-dependent values of solar radiation, ambient temperature, heat loss coefficient, useful heat flux, residual model error, and pump operating mode. The simulation results show that solar radiation intensity ranges from 0 to 860–880 W/m², with the active phase of solar exposure occurring approximately from 6:00 to 18:00, and maximum values being reached around midday. The ambient temperature varies between approximately 3 and 17 °C over the course of a day, while the heat loss coefficient ranges from 8.1 to 11.0 W/(m²·K). The system’s useful heat flux varies from 0 to 0.63–0.65 kW, reaching a maximum during the period of peak solar activity. It has been established that the system’s active heat output is primarily observed from 8:00 to 16:00. An analysis of the residual error between the calculated and measured temperature values showed that, in most cases, the deviations do not exceed approximately ±1.0 °C, which confirms the satisfactory adequacy of the developed model. Additionally, it is shown that the pump operates in a stable cyclic mode with binary states 0/1, ensuring periodic circulation of the coolant during the time interval under consideration. The results obtained confirm the feasibility of using the proposed model to predict the system’s energy efficiency, analyze pump control modes, and select optimal operating conditions for solar water-heating systems.

This article reviews the development and performance evaluation of a dual-loop solar hot water system featuring a vacuum tube collector, a phase-change heat exchanger, and automated control. The proposed system includes a vacuum solar collector with a multilayer selective coating, a composite capillary tube, a circulation pump with adjustable speed, an ethanol-based phase-change heat exchanger, temperature, pressure, and water level sensors, a microprocessor controller, protection against overheating and freezing, as well as a solar panel for off-grid power supply. The research methodology is based on thermal modelling of solar energy absorption processes, heat transfer in the primary and secondary loops, assessment of heat losses, the efficiency of the phase-change heat exchanger, and the overall system efficiency. The computational analysis was performed for a solar radiation range of 400–1000 W/m². The results show that as solar radiation increases, the useful thermal power rises from 554 to 1546 W, and the collector efficiency increases from 69.3% to 77.3%. The efficiency of the phase-change heat exchanger ranges from 89.9% to 93.0%, confirming stable heat transfer to the secondary loop. Compared to a conventional system, the proposed design provides an increase in collector efficiency from 65% to 77%, an increase in heat exchanger efficiency from 85% to 93%, and a reduction in heat losses from 180 to 108 W, which is approximately 40%. The results obtained confirm the promising application of an integrated dual-loop solar installation for energy-efficient and autonomous hot water supply.

This article discusses a hybrid photovoltaic/thermal PV/T system designed to simultaneously generate electrical and thermal energy using solar radiation. The main focus is on improving the system’s energy efficiency through the use of a CuO-ZnO-based hybrid nanofluid, which serves as the heat transfer fluid in the collector’s thermal circuit. The design of the PV/T module is presented, including a photovoltaic panel, a translucent thermal insulation coating, a chemically coated copper absorbent sheet, copper tubes for heat transfer fluid circulation, and a thermal insulation foundation. A measurement and control scheme is also described, providing monitoring of temperature, solar radiation, flow rate, pressure, voltage, and current. The calculated results show that with an increase in solar radiation from 420 to 980 W/m², the overall efficiency of the PV/T system increases from 42.0% to 54.1%. A comparison of working fluids showed that the CuO-ZnO hybrid nanofluid provides the highest overall efficiency of 57.1%, whereas for water this value is 50.7%. The optimal nanoparticle concentration is 0.5%, at which the best balance between efficiency improvement and pressure drop increase is achieved. The results confirm the potential of using CuO-ZnO nanofluid and an automated control loop to enhance the electrical and thermal performance of hybrid PV/T systems.

This paper presents an integrated predictive energy-management approach for a hybrid photovoltaic (PV)–battery–supercapacitor system supplying a permanent-magnet synchronous motor (PMSM) drive. The architecture combines maximum power point tracking, bidirectional DC-DC conversion, battery protection, supercapacitor-based transient support, DC-link regulation, and PMSM vector control. A MATLAB/Simulink model assessed power sharing, storage behaviour, DC-link dynamics, drive response, and conversion efficiency. Simulated PV power varied between 1.1 and 4.3 kW, battery state of charge remained near 65%, DC-link voltage stayed close to its reference, and overall efficiency reached approximately 92–97%. The results show that the proposed strategy reduces battery stress, compensates short-term power peaks, and supports stable drive operation under variable generation and load conditions.

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.