Prediction and evaluation of photovoltaic-thermal energy systems.pdf (3.98 MB)
Download filePrediction and evaluation of photovoltaic-thermal energy systems production using artificial neural network and experimental dataset
The current work aims to predict and assess a PV/T system using ANN models based on an experimental dataset in Oman. The PV/T system with weather station and data acquisition was installed in Sohar, Oman. The weather and electrical data has been recorded. A novel mathematical and ANN model for examining the performance of PV/T systems has been developed. The experimental results show improvement in PVT power production (68.6132 W) compared to the conventional PV (66.7827 W). The results demonstrate that the three proposed models (MLP, SOFM, and SVM) achieved excellent MSE results for generating the current values of the PV system (0.00043, 0.00030, 0.00041) and PV/T system (0.00719, 0.00683, 0.00763), respectively. Also, the proposed models delivered excellent MSE results for simulating the power values of the PV system (0.04457, 0.05006, 0.13816) and PV/T system (0.04457, 0.05006, 0.13816), respectively. The proposed models result validated with experimental data using descriptive statistics and Evaluation Metrics. Finally, the proposed neural models can generate future figures for any needed period that accurately fit the actual datasets.
History
Email Address of Submitting Author
acaapub@gmail.comORCID of Submitting Author
0000-0003-2110-8347Submitting Author's Institution
Sohar UniversitySubmitting Author's Country
- Oman