Up to 79%
more accurate than public weather forecasts
53%
fewer large errors in your temperature forecast
50%
average improvement in forecast accuracy
Xweather Optimize, part of the Xweather Insight weather confidence platform, delivers a more accurate forecast for the locations that matter most to your business. Optimize your operations for efficient energy and resource use and improve your decision-making with quality-controlled real-time weather data from on-site sensors and hyperlocal forecasts with calibrated confidence limits.
Optimization and efficiency
Improve your operational decision-making, resource use, and digital twin modeling with a more accurate short-term forecast with confidence limits powered by real-time, quality-controlled local observations.
More accurate where it matters most
Reduce large forecasting errors and better understand local variations in weather conditions at your key locations. Xcast technology delivers fresh forecasts on demand, so you always have the latest data.
All the benefits, none of the headaches
With Vaisala managing your sensors, you eliminate unexpected maintenance and servicing costs. The service includes remote monitoring, quality-controlled data, and a continuous hardware warranty.
Vaisala AtmoCast sensor network
Vaisala-managed network of wireless weather sensors with excellent accuracy, easy installation, and reliable year-round operation.
Xcast technology
Xcast technology combines your sensor observations with machine learning to improve the forecast at each location.
Xweather Optimize portal
Cloud-based UI for configuration and visualization and an API for integrating observations and forecasts into your daily operations.
Xcast sensors
Improve your operational decision-making with a network of Vaisala AtmoCast sensors installed at your business’s key locations. Our experts will assist you with site selection.
With Xweather managing your local sensor network, you eliminate unexpected maintenance and servicing costs. Remote monitoring, quality-controlled data, and a continuous hardware warranty are included in the service.
AtmoCast is a compact, all-in-one wireless weather sensor that offers industry-leading accuracy, easy installation, and hassle-free operation.
AtmoCast delivers exceptional accuracy using the same sensor technology found on the Mars Perseverance rover and trusted by meteorological agencies worldwide. Observations are quality controlled to ensure consistent, reliable data for the lifetime of the device.
Wireless NB-IoT communication for easy setup and connectivity
Battery powered with solar charging for all-year operation
Easy mounting in 15 minutes with no electrical connections required
Xcast technology
Xweather Optimize is powered by Xcast, a technology that delivers a more accurate forecast for the locations that matter to you and your business. Xcast uses machine learning, trained on your local sensor observations, to go beyond traditional weather forecasting, delivering more accuracy where it matters most and giving you the weather confidence that your business demands.
74% improvement
in temperature forecasting accuracy for a vineyard customer.
59% reduction
in large temperature forecast errors for a district heating customer.
50% improvement
in forecasting accuracy on average across all deployed devices.
Xcast gives you a more accurate picture of future conditions. Calibrated confidence limits for your forecast help you prepare for a range of scenarios rather than relying on single predicted value.
Xweather Optimize portal
Xweather Optimize includes a modern, cloud-based UI for configuring and managing the service and an API for integrating observations and forecasts into your daily operations.
View and analyze
View and analyze your real-time observations, historical measuements, and enhanced forecasts with intuitive visualizations.
API integration
Integrate measurements and forecasts directly into your applications via secure event streaming WebSocket API and history data REST API.
Remote monitoring
Monitor device and measurement status remotely and manage user roles and access rights.
Use cases
District heating
Reduce the uncertainty in your heat demand predictions and make savings in production, network management, and energy trading.
HVAC efficiency
Reduce energy costs and meet sustainability goals with AI-based predictive HVAC control for building energy management.
Dynamic line rating
Optimize grid capacity using real-time and forecasted weather data to implement dynamic line rating on high-voltage transmission lines.
Success story
Fortum is a Nordic energy company operating in areas where district heating is in high demand during the cold winters. Fortum worked with Vaisala Xweather to implement hyperlocal forecasting for its district heating network in Helsinki and its surrounding cities.
Challenge
There are significant variations in local weather conditions across Fortum’s large district heating network. Large differences in topography, vegetation, and the built environment, make accurate forecasting challenging.
Solution
Vaisala Xweather delivered a hyperlocal forecasting solution using a machine-learning model trained on real-time observations from a managed network of 26 weather sensors installed at key locations across Fortum’s network.
Results
Xweather Optimize improved the accuracy of Fortum's 6-hour forecast by up to 36% and reduced the number of errors greater than 2.5 °C in the 24-hour forecast by 59%.
"Our observation network of Vaisala weather stations provides us with accurate local data of critical weather parameters, enabling us to optimize heating supply temperature more precisely than before."
Viki Kaasinen
Head of Asset Digitalization
Fortum
Book a one-to-one guided demo to learn more about solving your weather challenges with Xweather Optimize.
Real-time observations
Quality-controlled data from a Vaisala-managed custom sensor network.
Enhanced forecasts with Xcast
Get a more accurate forecast for the locations that matter to you.
Easy integration
Modern cloud-based UI for configuration and an API for data integration.
What parameters does AtmoCast measure?
How are AtmoCast sensors installed and activated?
How long will it take to train Xcast on my observations data?