Optimising Own PV Consumption with PV Energy Yield Predictions from Machine Learning Algorithms and Weather Data

Heck, Horst; Schmidt, Armin Jürg; Schüpbach, Eva; Kuonen, Franziska; Bacha, Sania; Muntwyler, Urs (8 September 2020). Optimising Own PV Consumption with PV Energy Yield Predictions from Machine Learning Algorithms and Weather Data In: 37th European Photovoltaic Solar Energy Conference and Exhibition. Online. 7.-11. September 2020. 10.4229/EUPVSEC20202020

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Weather data are evaluated in view of their influence on high-quality PV energy yield predictions based on machine learning algorithms (MLAs). Optimisation experiments evidence that the prediction quality can be increased to over 30% by incorporating specific weather parameters in the ML-training. The results will feed into a planning tool for optimising the own consumption (including in wintertime) of PV plant owners. The outcome of this study also illustrates evolving best practice in using meteorological data to produce PV energy yield predictions with specific MLAs.

Item Type:	Conference or Workshop Item (Paper)
Division/Institute:	School of Engineering and Computer Science > Institut für Optimierung und Datenanalyse IODA School of Engineering and Computer Science > Institute for Energy and Mobility Research IEM > IEM / Photovoltaic systems
Name:	Heck, Horst0009-0007-9482-1705; Schmidt, Armin Jürg; Schüpbach, Eva; Kuonen, Franziska; Bacha, Sania and Muntwyler, Urs
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering
ISBN:	3-936338-73-6
Language:	English
Submitter:	Horst Heck
Date Deposited:	09 Nov 2020 11:30
Last Modified:	12 Dec 2023 21:46
Publisher DOI:	10.4229/EUPVSEC20202020
Related URLs:	Dataset
Uncontrolled Keywords:	Prediction, Machine Learning, Weather Data, PV Energy Yield
ARBOR DOI:	10.24451/arbor.13355
URI:	https://arbor.bfh.ch/id/eprint/13355