A new random vector functional link integrated with mayfly optimization algorithm for performance prediction of solar photovoltaic thermal collector combined with electrolytic hydrogen production system

Artificial intelligence techniques have become powerful alternatives to conventional modeling techniques in different engineering disciplines. They have been applied for modeling, control, prediction, optimization, forecasting, and identification of complex systems. In this paper, a novel optimized artificial intelligence method is developed to predict the performance of Photovoltaic/Thermal Collector (PVTC) incorporated with Electrolytic Hydrogen Production (EHP) system in terms of power output of PV, PV surface cell temperature, output temperature of cooling fluid, thermal and electrical efficiency, and hydrogen production yield. A new metaheuristic algorithm called mayfly based optimization (MO) algorithm has been implemented with Random Vector Functional Link (RVFL) network to maximize the prediction accuracy. The proposed hybrid artificial intelligence model was trained and tested using experimental data. The experiments were conducted outdoors for the proposed PVTC-EHP system operating with two different cooling fluids, namely, air and water under Indian weather conditions, and their results were compared with the predicted RVFL-MO and conventional RVFL results. Moreover, five statistical criteria were used to evaluate the performance of the investigated algorithms. The experimental results showed the hybrid PVTC-EHP system can produce a daily accumulated PV output power and hydrogen production yield of 1.66 kW/day and 3.60 kg/day for water-based PVTC-EHP system and 1.22 kW/day and 4.41 kg/day for air-based PVTC-EHP system, respectively, at a mass flow rate of 0.66 kg/min. Moreover, the statistical measures showed a perfect fit between the experimental and the proposed prediction model results. The results revealed that the root mean square error for the training phase of the RVFL and RVFL-MO was 0.25 and 0.65, respectively, while it was 1.63 and 2.04 for the testing phase, which reveals the important role of MO in determining the best parameters of RVFL network that maximize its prediction performance.