Hyperlocal forecasting for operational decisions
- 50% more accurate on average
- Site-specific machine learning
- Managed sensor service
Hyperlocal forecasting for operational decisions
Weather conditions can vary dramatically over short distances. Small forecast errors can increase energy costs or disrupt critical operations. Hyperlocal forecasts improve operational accuracy where every degree matters.
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Measurable forecast improvement within months
Xweather Optimize combines ground-truth observations from managed on-site sensors with dedicated machine learning models trained for each location. Forecasts improve continuously as more observations are collected, delivering greater local accuracy and fewer costly forecast errors.
- 50 %
average forecast improvement
- Up to 79 %
more accurate than public forecasts
- 59 %
fewer large errors in your temperature forecast
- 15 min
forecast refresh interval
40% of Fortune 100 rely on Xweather
Site-specific optimization
Forecast accuracy tuned to each location drives better operational decisions daily.
Fewer costly forecast errors
Dedicated models reduce the extreme forecast misses that cause the most damage.
Managed sensor service
A managed service handles sensor deployment, monitoring, maintenance, and data quality.
Confidence in every forecast
Forecast ranges communicate uncertainty alongside the forecast.
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At 05:10, the site read two degrees colder than the general forecast.
Every site deserves its own forecast
A 2°C error might mean nothing to a weather app. It can cost a district heating operator thousands in wasted fuel, or a grid operator a costly imbalance. Xweather Optimize trains a dedicated machine learning model on sensor data from each customer site, delivering forecasts calibrated to that exact location and updated as new data arrives.
- Dedicated ML models trained on local, ground-truth sensor data
- Forecasts delivered with confidence limits at every time step
- Managed sensor service with installation guidance, monitoring, and maintenance
Unlock grid capacity with dynamic line rating
Discover how to safely unlock more capacity from your existing transmission network with dynamic line rating. Join experts from Xweather and Siemens and see how real-time weather data and grid monitoring work together to support reliable, efficient, and secure grid operations.
Duration: 30 minutes
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Purpose-built for energy and infrastructure
Xweather Optimize serves operations where small forecast errors create large cost consequences. Each use case depends on accuracy at a specific location.
District heating operations
Hyperlocal temperature forecasts up to 50% more accurate than traditional sources, enabling district heating operators to fine-tune supply temperatures and reduce fuel consumption.
Building energy management
Building-level temperature forecasts that enable AI-based control systems to respond to actual conditions at each facility, reducing energy consumption while maintaining occupant comfort and supporting sustainability targets.
Dynamic line rating
Xweather Optimize captures local variations using AtmoCast sensors at critical spans, providing the precise, site-specific data that dynamic line rating calculations require to maximize grid throughput safely.
See Xweather Optimize in action
Book a demo and our team will show you how hyperlocal forecasts can improve your operations.
- Hyperlocal forecasting
- Managed sensor service
- Easy integration via portal and API
