{"year":"2024","publication":"IEEE PES ISGT  Europe 2024 Conference book","status":"public","publication_identifier":{"isbn":["978-953-184-297-6"]},"type":"conference","author":[{"last_name":"Schulte","first_name":"Katrin","full_name":"Schulte, Katrin","id":"221177"},{"full_name":"Engel, Lars","first_name":"Lars","last_name":"Engel"},{"full_name":"Quakernack, Lars","id":"221506","first_name":"Lars","last_name":"Quakernack"},{"first_name":"Fynn","last_name":"Liegmann","full_name":"Liegmann, Fynn","id":"231492"},{"full_name":"Haubrock, Jens","id":"205308","last_name":"Haubrock","first_name":"Jens"}],"date_updated":"2025-02-10T13:55:45Z","conference":{"start_date":"2024-10-14","location":"Dubrovnik, Croatia","name":"2024 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE)","end_date":"2024-10-17"},"date_created":"2025-02-07T12:55:58Z","user_id":"220548","project":[{"name":"Institut für Technische Energie-Systeme","_id":"0ec202b7-cd76-11ed-89f4-a9e1a6dbdaa7"}],"citation":{"ama":"Schulte K, Engel L, Quakernack L, Liegmann F, Haubrock J. A comparison of machine learning algorithms for the optimization of a day-ahead photovoltaic power forecast. In: Holjevac N, Baškarad T, Zidar M, Kuzle I, eds. <i>IEEE PES ISGT  Europe 2024 Conference Book</i>. ; 2024.","apa":"Schulte, K., Engel, L., Quakernack, L., Liegmann, F., &#38; Haubrock, J. (2024). A comparison of machine learning algorithms for the optimization of a day-ahead photovoltaic power forecast. In N. Holjevac, T. Baškarad, M. Zidar, &#38; I. Kuzle (Eds.), <i>IEEE PES ISGT  Europe 2024 Conference book</i>. Dubrovnik, Croatia.","alphadin":"<span style=\"font-variant:small-caps;\">Schulte, Katrin</span> ; <span style=\"font-variant:small-caps;\">Engel, Lars</span> ; <span style=\"font-variant:small-caps;\">Quakernack, Lars</span> ; <span style=\"font-variant:small-caps;\">Liegmann, Fynn</span> ; <span style=\"font-variant:small-caps;\">Haubrock, Jens</span>: A comparison of machine learning algorithms for the optimization of a day-ahead photovoltaic power forecast. In: <span style=\"font-variant:small-caps;\">Holjevac, N.</span> ; <span style=\"font-variant:small-caps;\">Baškarad, T.</span> ; <span style=\"font-variant:small-caps;\">Zidar, M.</span> ; <span style=\"font-variant:small-caps;\">Kuzle, I.</span> (Hrsg.): <i>IEEE PES ISGT  Europe 2024 Conference book</i>, 2024","ieee":"K. Schulte, L. Engel, L. Quakernack, F. Liegmann, and J. Haubrock, “A comparison of machine learning algorithms for the optimization of a day-ahead photovoltaic power forecast,” in <i>IEEE PES ISGT  Europe 2024 Conference book</i>, Dubrovnik, Croatia, 2024.","mla":"Schulte, Katrin, et al. “A Comparison of Machine Learning Algorithms for the Optimization of a Day-Ahead Photovoltaic Power Forecast.” <i>IEEE PES ISGT  Europe 2024 Conference Book</i>, edited by Ninoslav Holjevac et al., 2024.","chicago":"Schulte, Katrin, Lars Engel, Lars Quakernack, Fynn Liegmann, and Jens Haubrock. “A Comparison of Machine Learning Algorithms for the Optimization of a Day-Ahead Photovoltaic Power Forecast.” In <i>IEEE PES ISGT  Europe 2024 Conference Book</i>, edited by Ninoslav Holjevac, Tomislav  Baškarad, Matija  Zidar, and Igor  Kuzle, 2024.","short":"K. Schulte, L. Engel, L. Quakernack, F. Liegmann, J. Haubrock, in: N. Holjevac, T. Baškarad, M. Zidar, I. Kuzle (Eds.), IEEE PES ISGT  Europe 2024 Conference Book, 2024.","bibtex":"@inproceedings{Schulte_Engel_Quakernack_Liegmann_Haubrock_2024, title={A comparison of machine learning algorithms for the optimization of a day-ahead photovoltaic power forecast}, booktitle={IEEE PES ISGT  Europe 2024 Conference book}, author={Schulte, Katrin and Engel, Lars and Quakernack, Lars and Liegmann, Fynn and Haubrock, Jens}, editor={Holjevac, Ninoslav and Baškarad, Tomislav  and Zidar, Matija  and Kuzle, Igor Editors}, year={2024} }"},"language":[{"iso":"eng"}],"editor":[{"full_name":"Holjevac, Ninoslav","last_name":"Holjevac","first_name":"Ninoslav"},{"full_name":"Baškarad, Tomislav ","last_name":"Baškarad","first_name":"Tomislav "},{"first_name":"Matija ","last_name":"Zidar","full_name":"Zidar, Matija "},{"first_name":"Igor ","last_name":"Kuzle","full_name":"Kuzle, Igor "}],"_id":"5557","abstract":[{"text":"Model predicitive control (MPC) applications require accurate and reliable forecasts of local photovoltaic (PV) generation. This work presents a novel approach to optimize local PV power forecasts with little measurement data. Different machine learning (ML) methods are compared regarding accuracy for optimizing a day-ahead PV power forecast. With an nMAE of 4.90%, the k-nearest-neighbors (KNN) regression method shows the best results overall and is well below the nMAE of 6.31% compared to the initial forecast. The different ML methods have their advantages in different weather conditions. The optimization of the PV forecast is practicable, as only little measurement data of the PV power is required and the PV power forecast can be used directly in MPC applications.","lang":"eng"}],"title":"A comparison of machine learning algorithms for the optimization of a day-ahead photovoltaic power forecast"}