Digital Twins for Ice Rinks

The term digital twin has become increasingly common in manufacturing and industrial facilities. While the concept can sound complex, the idea is actually quite simple: create a virtual representation of a physical system that continuously updates using real-world data.

For ice rinks, a digital twin has the potential to transform how facilities are monitored, operated, and maintained. Rather than reacting to problems after they occur, operators can gain a live understanding of how their rink is performing, anticipate changing conditions, and make more informed operating decisions.

What Is a Digital Twin?

A digital twin is a software model of a real-world asset that stays synchronized with the physical system through continuous data collection.

Unlike a dashboard that simply displays sensor readings, a digital twin models how different parts of the facility interact. It combines live measurements, historical data, and mathematical models to estimate the current state of the system and forecast how it is likely to behave under changing conditions.

For an ice rink, this means creating a digital representation of the entire facility rather than viewing each sensor independently.

What Would an Ice Rink Digital Twin Include?

A comprehensive digital twin could combine information from throughout the facility, including:

• Ice surface temperature
• Concrete slab temperature
• Brine supply and return temperatures
• Refrigeration system performance
• Indoor air temperature and humidity
• Outdoor weather conditions
• Occupancy and scheduling
• Resurfacing events
• Energy consumption
• Equipment operating status

Individually, these measurements are useful. Together, they provide a much more complete picture of how the rink is operating and why conditions are changing.

Understanding Cause and Effect

One of the biggest advantages of a digital twin is its ability to connect cause and effect.

Consider a typical afternoon. A youth hockey practice ends, followed shortly by a public skate. More people enter the building, increasing both heat and moisture inside the arena. Two resurfacings occur within an hour while outdoor temperatures continue to rise.

Each of these events influences ice quality and refrigeration demand. Rather than treating them as unrelated events, a digital twin models how they interact. It can estimate how the ice surface temperature will respond, how refrigeration demand is likely to change, and how long it may take for the rink to return to normal operating conditions.

This provides a much deeper understanding than simply monitoring individual temperatures or equipment status.

Improved Maintenance

Digital twins can also help identify equipment issues before they become major problems. Because the software understands how the refrigeration system normally behaves, it can detect subtle deviations that might otherwise go unnoticed. Examples include:

• Heat exchangers gradually losing efficiency
• Refrigeration equipment operating outside normal ranges
• Sensor failures or calibration drift
• Unexpected increases in refrigeration demand
• Changes in slab thermal response over time

Rather than relying solely on scheduled maintenance, facilities can move toward condition-based maintenance, reducing unexpected downtime and improving equipment reliability.

Continuously Learning from the Facility

Unlike a static engineering model, a digital twin becomes more representative of the facility as it collects additional data.

Every resurfacing, weather event, tournament, and refrigeration cycle provides new information about how the rink responds under different operating conditions. Over time, the model develops a better understanding of the facility’s unique characteristics, including how quickly the slab warms, how occupancy affects humidity, and how seasonal weather influences refrigeration demand.

As more data becomes available, the digital twin increasingly reflects the actual behavior of the rink rather than relying solely on design assumptions.

Exploring “What-If” Scenarios

One of the most valuable capabilities of a digital twin is the ability to evaluate “what-if” scenarios without affecting the real facility.

Operators make decisions every day that influence energy consumption and ice quality. Should the slab be cooled earlier for an upcoming tournament? How much will a delayed resurfacing affect the ice? What happens if outdoor temperatures rise by 10°C this afternoon?

Rather than relying solely on experience, a digital twin can simulate these scenarios using current operating conditions and historical performance.

This allows facility staff to evaluate potential outcomes before making changes, reducing uncertainty and improving operational planning. As the model becomes more accurate over time, it becomes an increasingly valuable tool for testing operational strategies with minimal risk.

The Future of Ice Rink Operations

Digital twins are changing how industrial facilities are managed by moving beyond static monitoring toward software that can model, analyze, and better explain complex systems.

Ice rinks are particularly well suited to benefit from this approach. They operate in a dynamic environment where schedules, occupancy, weather, and resurfacing activities are constantly changing. A digital twin can continuously account for these factors, helping operators understand not only the current state of the facility but also how it is likely to evolve over the coming hours.

As sensing technology, cloud computing, and mathematical models continue to improve, digital twins will become increasingly capable of improving energy efficiency, supporting condition-based maintenance, maintaining consistent ice quality, and providing operators with better information for day-to-day decision making.

Ultimately, the value of a digital twin is not simply collecting more data. It is creating a better understanding of how an entire ice rink behaves so operators can manage it with greater confidence and insight.