Akademik Veri Yönetim Sistemi
Konya mühendislik bilimleri dergisi (Online), cilt.12, sa.2, ss.478-493, 2024 (ESCI)
Because there is a critical necessity to ensure the optimal operation of concentrated
photovoltaic-thermoelectric (CPV-TE) systems, various optimization methods such as Paretosearch (PS),
Multi-objective genetic algorithm (MOGA), and the hybrid Goal Attainment – Multi-objective genetic
algorithm (GOAL-MOGA) are commonly employed. These approaches aim to enhance both the output
power and energy efficiency of CPV-TE systems. By combining the Pareto fronts generated by MOGA
and GOAL-MOGA, 19 distinct machine learning (ML) algorithms were trained. The findings
demonstrate that the Artificial Neural Network (ANN) ML algorithm outperforms others, displaying an
average prediction error of 0.0692% on the test dataset. In addition to its prediction capability, the ANNbased ML model can be viewed as an optimization model since it produces optimized outputs similar to
those from MOGA and GOAL-MOGA. The ANN-based ML algorithm performs better when trained on
a combined dataset from both MOGA and GOAL-MOGA compared to using either MOGA or GOALMOGA alone. To enhance the optimization capability of the ANN-based ML algorithm further, more
Pareto fronts from other optimization techniques can be added.