Published in Solar Energy, Volume 207, 1 September 2020, pp. 1045-1054, https://doi.org/10.1016/j.solener.2020.07.043 © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ 1 Photovoltaic system monitoring for high latitude locations Mari B. Øgaard a,b* , Heine Nygard Riise b , Halvard Haug a,b , Sabrina Sartori a , Josefine H. Selj a,b a Department of Technology Systems, University of Oslo Gunnar Randers vei 19, 2007 Kjeller, Norway b Renewable Energy Systems Department, Institute for Energy Technology Instituttveien 18, 2007 Kjeller, Norway *Corresponding author. E-mail: mari.ogaard@its.uio.no, Tel.: +47 976 356 08 Reliable monitoring of PV systems is essential to establish efficient maintenance routines that minimize the levelized cost of electricity. The existing solutions for affordable monitoring of commercial PV systems are however inadequate for climates where snow and highly varying weather result in unstable performance metrics. The aim of this work is to decrease this instability to enable more reliable monitoring solutions for PV systems installed in these climates. Different performance metrics have been tested on Norwegian installations with a total installed capacity of 3.3 MW: i) comparison of specific yield, ii) temperature corrected performance ratio, and iii) power performance index based on both physical modelling and machine learning. The most influential effects leading to instability are identified as snow, low light, curtailment, and systematic irradiance differences over the system. The standard deviation of all the performance metrics is reduced when filters targeting these four effects are applied. Compared to general low irradiance or clear sky filtering, a greater reduction in the variation of the metrics is achieved, and more data remains in the useful dataset. The most suitable performance metrics are comparison of specific yield and performance index based on machine learning modelling. The analysis highlights two paths to accomplish increased reliability of PV monitoring systems without increased hardware costs. First, better reliability can be achieved by selecting a suitable performance metric. Second, the variability of the performance metric can be reduced by utilizing filters that specifically target the origin of the variability instead of using standard literature thresholds. Keywords: Photovoltaic systems; Monitoring; Filtering; Performance metric testing; Machine learning; High latitude climates 1. Introduction 1.1. PV system monitoring With recent years’ increased focus on operation and maintenance of photovoltaic (PV) systems related to its more important role in cost reduction (Klise et al., 2014; Whaley, 2016), numerous algorithms and performance metrics have been proposed to improve monitoring of PV installations (Daliento et al., 2017; Livera et al., 2019; Triki-Lahiani et al., 2018). The aim of these algorithms is to detect periods when the PV system is deviating from normal operation and identify faults. The existing solutions for affordable monitoring of commercial PV systems are however often inadequate for high latitude climates, as snow and highly varying weather result in unstable performance metrics. PV system monitoring is typically based on a comparison between the production data directly acquired from the inverter and a yield target (Daliento et al., 2017). Examples of this is yield comparison of similar units (Skomedal et al., 2019), performance ratio (PR) with or without