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Optimize wind farm outcomes and integrate into the grid

Advanced analytics provides optimization opportunities despite remote locations and uncontrollable variables for wind farms.

Transforming Wind Energy with AI

Critical insights for smarter wind farm operations

There are over 350,000 turbines globally, and wind farms are often located off-shore or in remote land-based locations. These wind farms are at the mercy of uncontrollable weather, and so energy production is not continuous. Wind farm operators balance priorities which include producing maximum energy, reducing operational power consumption, maintaining asset reliability and reducing maintenance costs.

Each turbine generates more than 500data points every ten minutes, meaning that wind farm operators have large volumes of data at their disposal. This data can be used to create AI models using Auto AI, to provide real-time critical insights for energy production optimization, power conservation and predictive maintenance.

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Product Features

Why our customers choose VROC

No-code AI

Produce AI models without any programming or coding experience

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VROC Predict Icon

Predictive Insights

Predict future events and outcomes, days, weeks or even months in advance

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Rapid results

Produce Ai models in minutes that refresh automatically with new data

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Anytime, Anywhere

No limit to the number of turbines you can monitor and analyse. Optimize your team with anywhere, anytime access

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Wind Turbine Use Cases

Optimizing Wind Energy

The optimization of operating modes for maximum power generation and the reduction of power consumption during low-wind periods are just two of the problem statements that engineers and subject matter experts can produce AI models for. Along with these, teams can produce models to help forecast future energy supply or compare turbine performances, with models produced in minutes, and deployed straight into a live production environment for real-time updates.

Both run-to-fail and preventative maintenance methods are high-cost maintenance strategies. Using AI, wind farm operators can implement predictive maintenance strategies with timelines for asset degradation and failure. VROC OPUS provides root cause analysis allowing operators to schedule maintenance in advance before a failure. This maintenance approach reduces maintenance costs and helps maintain asset reliability.

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Ai can assist wind farm operators during the first three years of the assets life, which are typically plagued with high failure rates. VROC OPUS can predict failures on new assets, with no prior failure history. The advanced automated AI technology continuously learns how the entire system functions under all conditions, helping wind farm operators maintain steady state from the beginning.

Energy producers with a diverse mix of energy production assets can benefit from the insights that can be learned across their entire portfolio using AI. VROC OPUS can help Energy companies optimize their portfolios, improve ESG outcomes, and help manage the grid with real-time monitoring, predictive analytics and forecasting across the entire network.

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AI Implementation

Time to value realisation

Client Setup

Client supply of historical data and set-up of real-time streaming

Week 1

Data ingestion and real-time data streaming connection

Week 2 & 3

Client training and model generation

Week 4

Models in production. Client starts delivering business value

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