Kraemer, Frank AlexanderFrank AlexanderKraemerAsad, Hafiz AreebHafiz AreebAsadBach, KerstinKerstinBachRenner, Bernd-ChristianBernd-ChristianRenner2023-05-262023-05-262023IEEE Access 11: 38417-38425 (2023)http://hdl.handle.net/11420/15350We explore the potential for predicting indoor photovoltaic energy on a forecasting horizon of up to 24 hours. The objective is to enable energy management approaches that exploit harvesting opportunities more strategically, for which they require more accurate energy intake predictions. Our study is based on a data set covering over 3 years, for which we simulate online machine learning algorithms with different amounts of training data and input features. Our results show that relatively simple machine learning methods can outperform a persistent predictor considerably, and we observed a reduction of errors of up to 56%. When devices obtain a significant amount of sunlight, adding the weather forecast improves the prediction accuracy. We discuss prediction features, the amount of training data and analyze the sources of errors to understand the potential of indoor photovoltaic energy harvesting predictions.en2169-353620233841738425IEEEhttps://creativecommons.org/licenses/by-nc-nd/4.0/energy harvestingphotovoltaic systemsenergy predictionsmachine learningInformatikTechnikIngenieurwissenschaftenJournal Article10.15480/882.515110.1109/ACCESS.2023.326781010.15480/882.5151Journal Article