Research Information System
International Journal of Thermofluids, vol.25, 2025 (Scopus)
Renewable energy sources are a suitable alternative to fossil fuels that solve the global warming issue. The electrical and thermal energy consumption can be met by integrating a photovoltaic panel and a thermal collector in one structure, making the photovoltaic thermal (PVT) systems. Moreover, the proposed approach gives electrical energy from two integrated PV modules, while integrated bifluid and airbased PVT collectors supply air and water as heated fluids for various usages representing the thermal energy production. This paper aims to utilize soft computing techniques in predicting the outlet fluids’ temperatures for monitoring based on the experience of outdoor experiments. The optimization was conducted using the Particle Swarm Optimization method to decide the rules of the Fuzzy Logic Controller. Furthermore, combining two techniques gives the best predictions compared to actual data. The results of the comparison showed a satisfactory correspondence between the predicted and the experimental data. The maximum outlet fluid temperature for Case A (60.3°C) was predicted as 54.7°C, constituting a relative error of 7.0 % in the case of cooling one. For Case B, the maximum outlet fluid temperature (50.2°C) was predicted 45.7 %, constituting a 5.5 % relative error. For the reliability of the proposed algorithm, the mean absolute percentage error was used for error estimation detection. The results from the error estimation method have varied from 3.5 % to 9.3 %, which makes the prediction quite near to the actual results.