Original Article

Analyzing and modeling snow loss and snow loss accumulation in ground-mounted photovoltaic systems

¹ Department of Solar Power Systems, Institute for Energy Technology, Kjeller 2007, Norway
² University of Agder, Department of Engineering Sciences, Grimstad 4879, Norway
³ PVRADAR Labs GmbH, Ebersberg 85560, Germany

^* e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Received: 30 September 2025
Accepted: 27 January 2026
Published online: 16 February 2026

Abstract

Snow can cause significant energy loss in photovoltaic systems, making it essential to account for in performance modeling for all snow-prone regions. Previous snow loss research has been conducted primarily for roof mounted systems, and there is a lack of reported snow losses and validation of snow loss models for ground mounted systems. The aim of this work is to quantify snow loss and validate the Marion snow loss model for ground mounted PV systems using data from two scientific test sites in Norway. The quantification of snow losses indicates that ground mounted systems have lower snow losses compared to what have previously been documented for roof mounted systems in Norway and similar climates. Additionally, monofacial and bifacial arrays experienced similar losses. At the same test site, the bifacial array’s monthly losses were, on average, 0.2 percentage points higher than those of the monofacial array. In the snow loss model validation, the impact of snow data quality was evaluated. The global weather snow data sources assessed (ERA5-Land and ERA5 global) had a large share of snowfall events not giving snow accumulation. These events were linked to both small snowfalls and higher temperatures. The default pvlib implementation of the Marion model typically underestimates losses, but the error in relative yearly losses is less than 0.5 percentage points. The error in the modeling is mainly related to periods where the temperature is close to zero, when both the snow input data and the prediction of snow shedding are more uncertain. Ground interference due to large snow depths also leads to slower shedding and underestimation of losses in the model.