Outdoor PV system monitoring—input data quality, data imputation and filtering approaches

Lindig, Sascha (avtor), Louwen, Atse (avtor), Moser, David (avtor), Topič, Marko (avtor)

Photovoltaic monitoring data are the primary source for studying photovoltaic plant behavior. In particular, performance loss and remaining-useful-lifetime calculations rely on trustful input data. Furthermore, a regular stream of high quality is the basis for pro-active operation and management activities which ensure a smooth operation of PV plants. The raw data under investigation are electrical measurements and usually meteorological data such as in-plane irradiance and temperature. Usually, performance analyses follow a strict pattern of checking input data quality followed by the application of appropriate filter, choosing a key performance indicator and the application of certain methodologies to receive a final result. In this context, this paper focuses on four main objectives. We present common photovoltaics monitoring data quality issues, provide visual guidelines on how to detect and evaluate these, provide new data imputation approaches, and discuss common filtering approaches. Data imputation techniques for module temperature and irradiance data are discussed and compared to classical approaches. This work is intended to be a soft introduction into PV monitoring data analysis discussing best practices and issues an analyst might face. It was seen that if a sufficient amount of training data is available, multivariate adaptive regression splines yields good results for module temperature imputation while histogram-based gradient boosting regression outperforms classical approaches for in-plane irradiance transposition. Based on tested filtering procedures, it is believed that standards should be developed including relatively low irradiance thresholds together with strict power-irradiance pair filters.

• Jezik: Angleški jezik
• Ključne besede: photovoltaics, solar cells, photovoltaic system performance, photovoltaic system data, data quality, data imputation, data filtering
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FE - Fakulteta za elektrotehniko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2020
• Št. strani: 18 str.
• Številčenje: Vol. 13, iss. 19, art. 5099
• PID: 20.500.12556/RUL-134484
• UDK: 621.383.51
• ISSN pri članku: 1996-1073
• DOI: 10.3390/en13195099
• COBISS.SI-ID: 32452611
• Datum objave v RUL: 18.01.2022

Gradivo je del revije

• Naslov: Energies
• Skrajšan naslov: Energies
• Založnik: Molecular Diversity Preservation International
• ISSN: 1996-1073
• COBISS.SI-ID: 518046745

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
• Začetek licenciranja: 01.10.2020

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: fotovoltaika, sončne celice