Identifying snow in photovoltaic monitoring data for improved snow loss modeling and snow detection

Mari B Øgaard,Bjørn L Aarseth,Åsmund F Skomedal,Heine N Riise,Sabrina Sartori,Josefine H Selj

doi:10.1016/j.solener.2021.05.023

Mari B Øgaard, Bjørn L Aarseth + Show 4 more

Open Access

https://doi.org/10.1016/j.solener.2021.05.023

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Abstract

As cost reductions have made photovoltaics (PV) a favorable choice also in colder climates, the number of PV plants in regions with snowfalls is increasing rapidly. Snow coverage on the PV modules will lead to significant power losses, which must be estimated and accounted for in order to achieve accurate energy yield assessment and production forecasts. Additionally, detection and separation of snow loss from other system losses is necessary to establish robust operation and maintenance (O&M) routines and performance evaluations.Snow loss models have been suggested in the literature, but developing general models is challenging, and validation of the models are lacking. Characterization and detection of snow events in PV data has not been widely discussed.In this paper, we identify the signatures in PV data caused by different types of snow cover, evaluate and improve snow loss modeling, and develop snow detection. The analysis is based on five years of data from a commercial PV system in Norway. In an evaluation of four snow loss models, the Marion model yields the best results. We find that system design and snow depth influence the natural snow clearing, and by expanding the Marion model to take this into account, the error in the modeled absolute loss for the tested system is reduced from 23% to 3%. Based on the improved modeling and the identified data signatures we detect 97% of the snow losses in the dataset. Endogenous snow detection constitutes a cost-effective improvement to current monitoring systems.

Highlights

Due to a substantial decline in the price of photovoltaic (PV) in stallations in recent years, large scale PV plants are increasingly com mon in cold climates with wintertime snowfalls (Burnham et al, 2020; Hashemi et al, 2020; IEA, 2020; Jager-Waldau, 2020)
Snow coverage on the PV modules will lead to significant power losses, which must be estimated and accounted for in order to achieve accurate energy yield assessment and production forecasts
We identify the signatures in PV data caused by different types of snow cover, evaluate and improve snow loss modeling, and develop snow detection

Summary

Introduction

Due to a substantial decline in the price of photovoltaic (PV) in stallations in recent years, large scale PV plants are increasingly com mon in cold climates with wintertime snowfalls (Burnham et al, 2020; Hashemi et al, 2020; IEA, 2020; Jager-Waldau, 2020). This develop ment necessitates robust methods for analyzing PV yield and perfor mance, as well as flexible monitoring and forecasting solutions in snowy conditions. When using empirical or machine learning based methods for PV modeling, snow events in the training data will perturb the correlations between irradiance, temperature and production. These perturbations can increase the uncertainty of the models (Øgaard et al, 2020)

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