Optimal development of location and technology independent machine learning photovoltaic performance predictive models

Abstract

Photovoltaic (PV) power prediction is important for monitoring the performance of PV plants. The scope of this work is to develop a methodology for deriving an optimized location and technology independent machine learning (ML) model for power prediction. The prediction accuracy results demonstrated that the performance of the ML model was primarily affected by the dataset split method. In particular, for a 70:30 % train and test set approach, the ML model achieved a normalized root mean square error (nRMSE) of 0.88 % when using randomly selected samples compared to 0.94 % when using continuous samples. The accuracy of the developed model was also affected by the duration of the train set. For a random 70:30 % train and test set approach, the constructed ML topology achieved a nRMSE of 0.88 %, while when the dataset was split into a 30:30 % portion, the nRMSE was 0.95 %. Moreover, when low irradiance conditions were filtered out and 70 % of the entire dataset was randomly chosen for model training, a nRMSE of 1.41 % was obtained demonstrating that the model's accuracy was not improved. Finally, for a random 10:30 % train and test set approach, the FNNN achieved the lowest nRMSE of 1.10 % when the model was trained using the prevailing irradiance classes.