Heck, HorstHorstHeckMuntwyler, UrsUrsMuntwylerSchüpbach, EvaEvaSchüpbach2024-11-192024-11-192021-09-063-936338-78-710.4229/EUPVSEC20212021-6CO.11.2https://arbor.bfh.ch/handle/arbor/43105Prediction of PV power is useful to estimate and plan power production, net stability, and own consumption. Input data for the predictions are physical parameters like solar irradiation (horizontal or inclined), temperature (of air and PV module), etc. To identify such input parameters, several methods have been proposed in the open literature. Physical models, statistical models, or a machine learning approach can be used to predict PV power. Here, we developed our own machine learning (ML) algorithm and trained it with AC-power data from our own PV monitoring network in Switzerland. Results are presented on how to optimize our algorithm in view of obtaining a precise prediction for PV power production. Such information is important for owners of PV plants to steer their own production/consumption. Especially own consumption of solar electricity in winter needs to be maximised, as PV will be enforced to successfully implement the Swiss Energy Strategy 2050.enQATKconference_item