Machine Learning Based PV Power Prediction Using Different Environmental Parameters of Turkey
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Keywords:
PV power prediction, Machine learning, Environmental parameters, MLP, CNNAbstract
The prediction of photovoltaic power generation provides the basis for the generation,
transmission, and distribution systems of electrical energy, ensuring the establishment of uninterrupted and
reliable energy systems. In the present study, environmental parameters and power values produced from
photovoltaic panels were measured and recorded for 1 year with the measurement stations established in
three different regions (Adıyaman-Malatya-Şanlıurfa) in terms of environmental parameters. Modeling has
been developed for the power estimation to be produced using the MLP, CNN, LSTM, Stacked LSTM,
Bidirectional LSTM, and CNN-LSTM methods on the extensive dataset. Predictions were obtained from
the developed models with an accuracy rate of 98.9%, 98.6%, 95.1%, 95.0%, 95.0%, and 84.9%,
respectively. As a result of the study, it has been seen that all of the proposed methods are successful for
the problems of PV power prediction. In addition, it has been determined that the success rate of MLP and
CNN methods is superior to other methods. Thus, with the developed forecasting models, PV power
prediction for photovoltaic power systems desired to be installed by using environmental parameters
belonging to different regions in any part of the world can be estimated with a high degree of accuracy.
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