Technology
Enterprise-grade hyper-local forecasting built for energy
Enterprise-grade hyper-local forecasting built for energy
Traditional weather forecasts are designed for cities and regions, not for individual assets. hylosense builds a dedicated machine learning model for each variable at each location, continuously calibrated with your sensor data to capture the micro-climate dynamics that generic forecasts miss.
Every energy asset operates in a unique micro-environment. Terrain, elevation, nearby structures, and local atmospheric patterns all influence weather conditions at a specific location. Our architecture reflects this reality: we deploy dedicated ML models that learn the specific characteristics of each site.
Each asset gets its own model trained on historical data and continuously refined with real-time observations from that exact location.
Wind speed, irradiance, and temperature each have different drivers. We optimize separate models for each variable to maximize accuracy.
Models continuously ingest new sensor readings, adapting to seasonal shifts, equipment changes, and evolving site conditions.
Why it matters: A wind farm in complex terrain can see 15-20% variation in wind speed across turbines just hundreds of meters apart. Generic forecasts can't capture this; our per-location models can.
Your existing on-site sensors become the foundation for unprecedented forecast accuracy. We integrate with SCADA systems, met masts, pyranometers, and other instrumentation to create a continuous feedback loop between predictions and ground truth.
Connect via our JSON REST API or integrate with your existing data platforms. No hardware changes required.
Automated anomaly detection flags sensor drift, outages, and data quality issues before they impact model performance.
Every observation refines the model. Forecast errors are analyzed and used to improve predictions within hours, not months.
Result: Clients typically see 30-50% improvement in forecast accuracy compared to their existing weather provider within the first 30 days of calibration.
We provide the complete set of meteorological parameters required for energy operations, from real-time nowcasts to 14-day forecasts, all at your asset's exact coordinates.
Ambient and panel temperature forecasts affecting efficiency and demand
Barometric pressure for air density calculations and storm tracking
Relative humidity affecting equipment performance and corrosion risk
Hub-height wind speeds at 10m, 80m, 100m+ elevations with directional forecasts
Rain and ice accumulation forecasts for operations planning
Total and layered cloud coverage percentages for solar forecasting
Snow accumulation and intensity forecasts for winter operations and panel coverage
GHI, DNI, and DHI components for accurate PV output modeling
Built for enterprise integration from day one. Our RESTful API delivers forecasts directly to your trading systems, SCADA platforms, or custom applications with minimal development effort.
Clean JSON endpoints with comprehensive documentation. Easy to integrate with any language or platform.
JSON responses by default, with CSV exports available. Webhook support for real-time updates.
Access 24 to 48-hour ahead forecasts updated hourly for operational planning.
POST /api/forecast-data
Content-Type: application/json
{
"key": "your-api-key",
"location": "wind-farm-north",
"timezone": "Europe/London"
}{
"model": "hylosense_model",
"latitude": 53.3,
"longitude": -0.78,
"timezone": "Europe/London",
"hourly_units": {
"time": "iso8601",
"temperature_2m": "°C",
"pressure_msl": "hPa",
"relative_humidity_2m": "%"
},
"hourly": {
"time": ["2026-01-06T01:00", "2026-01-06T02:00", ...],
"temperature_2m": [9.97, 9.16, ...],
"pressure_msl": [998.28, 997.87, ...],
"relative_humidity_2m": [80.3, 88.0, ...]
}
}See how hylosense integrates with your infrastructure. We'll walk through the API, discuss your specific use case, and show you real accuracy improvements from similar deployments.