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Photovoltaic energy yield prediction using an irradiance forecast model based on machine learning for decentralized energy systems

 
: Wendlandt, Stefan; Popescu, Florin

:
Volltext urn:nbn:de:0011-n-6059915 (10 MByte PDF)
MD5 Fingerprint: 81c70a0412bc6db2a53549c2d877ac2b
Erstellt am: 3.11.2020


36th European Photovoltaic Solar Energy Conference and Exhibition, EU PVSEC 2019 : Proceedings of the international conference held in Marseille, France, 09-13 September 2019
Marseille, 2019
ISBN: 3-936338-60-4
S.1860-1864
European Photovoltaic Solar Energy Conference and Exhibition (EU PVSEC) <36, 2019, Marseille>
Bundesministerium fur Wirtschaft und Energie BMWi (Deutschland)
03ET1312; WaveSave
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer FOKUS ()
forecast; energy yield; energy management

Abstract
Over the past few years electricity generation costs for PV technology have dropped massively. Since, at the same time, PV module efficiencies have increased significantly, the market for building-applied PV systems has dramatically changed and in many countries it has become a de facto standard to use PV as the main source for the building´s energy needs. Because the power output of PV systems is fluctuating along with solar irradiation, advanced energy storage and management systems are necessary to cover the building energy demand on a stable basis. This paper presents a novel ‘gray-model’ approach to the estimation the forecast of PV energy systems. It is based on machine learning for solar irradiance forecasting and physical-mathematical models to simulate the PV system itself. The paper presents a comparison between simulated and real-life energy production data of a sample PV system.

: http://publica.fraunhofer.de/dokumente/N-605991.html