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Xweather Optimize
Local forecast accuracy that drives operational performance
- 50% more accurate on average
- Managed sensor service
- Calibrated confidence limits
Machine learning forecasts with calibrated confidence limits, powered by ground-truth observations from sensors deployed and maintained at your sites.
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Measurable forecast improvement within months
Xweather Optimize pairs on-site sensors with a dedicated machine learning model for each location. The model typically needs three to six months of observations before delivering measurably improved local forecasts. Accuracy continues to improve as more data arrives.
0 %
average forecast improvement
Up to 0 %
more accurate than public forecasts
0 %
fewer large errors in your temperature forecast
A dedicated forecast for each location
Every Xweather Optimize deployment combines wireless IoT sensors with a dedicated machine learning forecast model trained on that site's observed data. Forecast accuracy improves continuously as the observation dataset grows.
- Site-specific models
- Wireless sensors
- API and dashboard
Optimization at every site
Forecast accuracy tuned to each location drives better operational decisions daily.
Fewer costly large errors
Dedicated models reduce the extreme forecast misses that cause the most damage.
No operational burden
A managed service handles sensor deployment, monitoring, maintenance, and data quality. Installation is straightforward, with no extensive infrastructure, cabling, or specialist contractors required.
Confidence in every forecast
Calibrated confidence limits quantify forecast uncertainty, giving operations teams clear parameters for decisions.
Always the latest information
Xweather Optimize refreshes forecasts as new sensor observations arrive, rather than waiting for the next global model cycle. Calibrated confidence limits show both the forecast value and the range of likely outcomes at 50%, 80%, 95%, and 99% levels.
- Forecasts update every 15 minutes
- Calibrated confidence limits with range of likely outcomes
- Fresh forecasts on demand
Hyperlocal forecasting 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.
Dynamic line rating
Dynamic line rating
Transmission capacity depends on real-time wind speed, direction, and ambient temperature along each span.
Conditions vary dramatically even across short distances, making generic forecasts inadequate for safe capacity increases.
Xweather Optimize captures these 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.
Unlock grid capacity with dynamic line rating
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District heating
District heating operations
Weather is the single biggest factor affecting daily heating demand. Even small temperature forecast errors translate directly into wasted energy or supply shortfalls.
Xweather Optimize delivers hyperlocal temperature forecasts up to 50% more accurate than traditional sources, enabling district heating operators to fine-tune supply temperatures and reduce fuel consumption.
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Building energy management
Building energy management
Predictive HVAC control depends on accurate short-term temperature forecasts to pre-cool or pre-heat buildings ahead of weather shifts. Generic city-level forecasts introduce errors that reduce efficiency gains.
Xweather Optimize provides 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.
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Reduce large forecast errors
59% fewer errors greater than 2.5°C in 24-hour forecasts at customer sites.
All the benefits, none of the headaches
Vaisala manages sensors, you focus on operations.
Proven across industries
District heating, HVAC, grid operators, and more.
Sensors, models, and confidence limits
Xweather Optimize combines three elements into a single managed service: precision sensors, a dedicated forecast model for each site, and calibrated confidence limits that quantify uncertainty for operational decisions.
- AtmoCast sensors
Wireless IoT sensors with industry-leading accuracy, easy installation, and hassle-free operation. Measures air temperature, wind speed, wind gusts, wind direction, rain accumulation, atmospheric pressure, humidity, dew point, and hail. The same sensor technology used on the Mars Perseverance rover.
- ML forecasts
A dedicated machine learning model trained on each site's observed data. Models improve continuously as more observations arrive and refresh on demand, delivering forecasts calibrated to local conditions.
- Confidence limits
Calibrated probability bounds at 50%, 80%, 95%, and 99% confidence levels. Operations teams see both the forecast value and the range of likely outcomes, enabling decisions proportional to the certainty of the prediction.
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Xweather Optimize is a managed service. Xweather handles the complexity so your team focuses on operations.
01
Assess
Xweather evaluates your site conditions and operational requirements to determine optimal sensor placement and forecast model configuration for each location.
02
Deploy
Vaisala deploys AtmoCast wireless sensors at your sites. Installation is straightforward, with no extensive infrastructure, cabling, or specialist contractors required.
03
Train the local model
The dedicated machine learning model begins learning local weather patterns from incoming sensor data. Forecast accuracy improves continuously as the model adapts to site conditions.
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Deliver forecasts and data
Calibrated forecasts and quality-controlled observations are delivered through the Xweather Optimize dashboard and API, ready for integration into your operational systems.
Security and quality built in
Certified to industry standards, ensuring trusted data, safe operations, and reliable performance.
ISO
27001
Standard for security governance
ISO
9001
Standard for quality management systems
SOC 2
Compliant in secure data management
Platform capabilities
Xweather Optimize provides a unified platform for viewing observations, accessing forecasts, managing devices, and integrating data into operational systems.
View and analyze
Real-time observations, historical measurements, and enhanced forecasts displayed through map, graph, and list views. Industry-leading accuracy with up to 79% improvement over public weather services.
Dashboard viewsHistorical analysis
API integration
WebSocket streaming for real-time data. REST API for historical records. Secure and documented.
WebSocket and RESTSecure and documented
Remote monitoring
Monitor device and measurement status remotely. Xweather manages sensor health and data quality. Customers manage user roles and access rights through the platform.
Remote accessDevice health
Confidence limits
Calibrated forecast ranges help you prepare for multiple scenarios, not just the most likely outcome.
Probability boundsMultiple confidence levels
Everything from a single provider
Xweather Optimize delivers sensors, forecasts, data quality management, and ongoing maintenance from a single provider. Vaisala manufactures the sensors. Xweather deploys, monitors, and maintains them. A dedicated forecast model is trained on each site's data and delivered through a unified dashboard and API.
This integrated approach eliminates the complexity of sourcing sensors from one vendor, forecasts from another, and building integration and data quality processes internally. No combination of separate providers can replicate it.
See Xweather 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
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Webinars and demos
Frequently asked questions
Through Xcast sensors and supported stations, Optimize can access more than 30 weather and environmental parameters via the shared Xweather Observe framework, including core variables such as air temperature, wind speed, humidity, and other surface conditions. Forecasting in Optimize focuses on parameters where hyperlocal accuracy has the largest operational impact, particularly air temperature and wind speed, but the platform also provides broader condition and forecast data for up to ten days at any point globally via its forecast and condition endpoints.