High-Resolution ML Time Series Modelling
Develop long-term hub height time series data for accurate project feasibility, yield forecasting, resource mapping, and hybrid optimization using WindML time series services.
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WindML Full Subhourly
High resolution Machine Learning (ML) powered subhourly time series designed for 10, 20, or 25 years including hub height wind speed, wind direction, temperature, wind speed standard deviation (turbulence)
WindML Full Hourly
ML powered MCP solution which outputs hourly time series designed for 10, 20, or 25 years including key variables
WindML TMY
Typical Meteorological Year (TMY) time series - designed for efficient hybrid optimization for use in combination with solar/storage
Salient Features
High-Resolution Wind Time Series Data for Accurate Wind Resource and Hybrid Assessment
Accuracy
Long term time series output which greatly improves both point predictions and distributional accuracy.
Machine Learning
Advanced machine learning algorithms such as neural networks and deep learning unlock full potential of MCP and subhourly data.
Key variables and Tailored solution
Time series set covering four key variables required for wind resource assessment - wind speed, wind direction, temperature and wind speed standard deviation (turbulence).
Preferred temporal resolution (subhourly/hourly) and duration (10/20/25 years) or typical meteorological year (TMY) to meet specific requirements
Elevate Your Wind Assessment Today
Ready to unlock full potential of your wind data through Machine Learning?
We are actively engaging with clients that require a sophisticated upgrade from traditional wind resource assessment (WRA) to achieve a higher fidelity time series modelling.