Windscape AI utilizes machine learning to process data from a low-cost Wi-Fi mesh network of sensors, providing 15-60 second nowcasts of wind changes to optimize turbine pitch, yaw, and torque settings. This technology addresses the inefficiencies of current wind turbines, enhancing energy capture and extending the lifespan of critical components, resulting in increased revenue and improved project ROI.
Funding
$700K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
TBFounders
Product
Problem
Current wind turbines often operate suboptimally due to limited real-time data on local wind conditions, leading to inefficiencies in energy capture and increased stress on critical components. Existing data sources and onboard systems are not fully leveraged to adapt to rapidly changing wind patterns.
Solution
Windscape AI provides near real-time (15-60 second) wind forecasts using data from a low-cost, distributed Wi-Fi mesh network of sensors deployed throughout a wind farm. Proprietary machine-learning algorithms process this data to predict short-term wind changes, enabling turbines to proactively adjust pitch, yaw, and torque settings. This optimization enhances energy capture, reduces mechanical stress, and extends the lifespan of turbine components, ultimately increasing revenue and improving project ROI for wind farm operators.
Target Audience
The primary target audience includes wind farm operators and owners seeking to improve turbine efficiency, reduce maintenance costs, and maximize energy production.
Features
- Low-cost, easily deployable Wi-Fi mesh network of wind sensors
- High-resolution, localized wind nowcasts updated every 15-60 seconds
- Patented machine-learning algorithms for accurate short-term wind prediction
- Real-time turbine control optimization based on predicted wind conditions
- Integration with existing turbine control systems for seamless operation