From Frost to Forecast: Protecting Wind Energy in Cold Climates

Ice accretion on wind turbine blades can have a major impact on energy production. Scientific studies show that ice changes blade shape and surface roughness, degrading aerodynamic performance. This buildup increases drag and reduces lift, limiting the turbine’s ability to capture wind energy. In cold-climate regions, field evidence indicates that annual energy losses due to icing can exceed 20–30 % of total output. 

Beyond reduced production, ice can cause severe operational disruptions such as ice throw and ice-induced vibrations, leading to safety concerns, and potential damage to turbine structures. Field measurements on utility-scale turbines have documented that, during icing events, turbines may rotate much more slowly or even stop entirely.In one case study, ice accumulation led to up to 80 % loss in power production during the event, causing substantial deviations from day-ahead forecasts and resulting in increased imbalance costs.

Numerical fluid dynamics studies confirm that ice changes blade lift and drag characteristics, creating operating points that further reduce power output.

Ice Events Are a Recognized Operational Challenge in Cold Climates

Wind turbines operating in cold climate regions experience icing frequently enough that it is considered a significant operational issue for wind energy generation. Ice accretion occurs not only due to environmental conditions but also because variations in flow and turbine layout influence where and how ice forms on blades. 

To better anticipate these risks, probabilistic forecasting has been shown to improve prediction skill. Studies comparing deterministic forecasts to probabilistic approaches demonstrate that incorporating uncertainty increases forecast accuracy for ice-related production losses. Ensemble forecasts, which quantify the spread of possible outcomes, provide operators with valuable information about the likelihood of severe production impacts.

Recent research also shows that machine learning models, trained on meteorological and turbine data, can accurately identify and predict icing status days in advance. These models use variables such as temperature and wind speed to forecast conditions under cold climates. 

Implications for Wind Operations

The evidence is clear: 

• Ice formation degrades turbine aerodynamic performance and reduces energy output
• When ice accumulates, turbines may slow down or shut down, causing major production losses 
• Quantifying uncertainty in icing forecasts with probabilistic methods provides more reliable anticipation of production losses than single deterministic forecasts
• Advanced models, including machine learning approaches, have been demonstrated to predict icing conditions ahead of time using weather and turbine data 

How Renewcast Can Help 

Building on these established insights, Renewcast delivers probabilistic ice forecasting tailored for wind energy operations that:

• Integrates weather prediction with turbine performance data,
• Quantify likelihood and uncertainty of icing conditions,
• Offers actionable insights that help operators anticipate production losses and prepare operational strategies ahead of cold events.

Rather than reacting to ice events after they occur, this type of forecasting allows wind operators and energy traders to plan with confidence.

References

MDPI, 2025

PubMed, 2021

Munin, 2021

DOAJ, 2023

ScienceDirect, 2025