Using artificial neural networks to enhance the accuracy of the photovoltaic simulation model
| Autor: | Kamal Al Khuffash, Y.L. Abdel-Magid, L. A. Lamont |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Engineering
Artificial neural network Open-circuit voltage business.industry 020209 energy Computer Science::Neural and Evolutionary Computation Photovoltaic system 02 engineering and technology Renewable energy 0202 electrical engineering electronic engineering information engineering Electronic engineering business Constant (mathematics) Short circuit Simulation |
| Zdroj: | 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe). |
| DOI: | 10.1109/eeeic.2017.7977458 |
| Popis: | This study aims to enhance the accuracy of the PV simulation model by using Artificial Neural Networks (ANNs) to incorporate the effect of the surrounding environment in the model. The ANNs designed are using the weather aspects to predict the actual values of Short Circuit Current (Isc) and Open Circuit Voltage (Voc) used in the simulation. The results show that varying the values of Isc and Voc using ANNs does enhance the prediction accuracy. Therefore, it is concluded that the values of Isc and Voc should not be used as constant in the simulation of the PV panel. In addition, ANNs are found to be a valuable tool to be used in the simulation of PV and their use could be extended to the simulation of other renewable energy sources. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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