Vaisala’s Xweather Optimize Helps District Heating Providers Save €300,000 Annually by Improving Weather Forecast Accuracy

Vaisala’s Xweather Optimize Helps District Heating Providers Save €300,000 Annually by Improving Weather Forecast Accuracy

(IN BRIEF) Vaisala’s Xweather Optimize enhances the accuracy of weather forecasts for district heating providers, leading to savings of up to €300,000 annually. By improving forecast accuracy, district heating operators can optimize energy production and distribution, reduce operational costs, and better participate in energy trading.

(PRESS RELEASE) VANTAA, 30-Apr-2025 — /EuropaWire/ — Vaisala, a global leader in environmental measurement technology, has introduced Xweather Optimize, a cutting-edge solution designed to improve weather forecast accuracy for district heating providers. By providing hyperlocal, network-specific weather forecasts tailored to each district heating network, Xweather Optimize enables more precise energy production and distribution, leading to significant cost savings.

District heating operators rely heavily on accurate weather forecasts to optimize heat production and delivery. By minimizing forecasting errors, Xweather Optimize helps reduce unnecessary boiler activations, adjust input water temperatures more precisely, and apply dynamic safety margins, all while ensuring efficient energy delivery, even to the most distant buildings. The solution also enables district heating providers to participate confidently in reserve markets to help balance the grid.

“By minimizing weather forecasting errors, we minimize heat prediction errors and enable companies to optimize cost-efficient heat production and distribution,” said Jussi Paanajärvi, Product Manager at Vaisala for Xweather Optimize. “Improved accuracy in the 1-to-48-hour forecasts also helps energy traders reduce the need to correct shortfalls and surpluses in the imbalance market.”

Xweather Optimize is already making a significant impact in Finland, where it is being used by district heating providers such as Fortum. The solution reduced forecasting errors greater than 2.5°C by 59%, resulting in substantial savings across several key areas, including heat production, network control, and energy trading.

Key savings include:

  • €120,000 saved by optimizing heat production and minimizing unnecessary peak boiler initiations.
  • €80,000 saved with effective network control by managing local constraints and bottlenecks.
  • €100,000 saved in energy trading with more accurate bids and reduced need to correct shortfalls and surpluses in imbalance markets.

Xweather Optimize uses an easy-to-install wireless weather sensor network combined with software that continuously improves forecasts using machine learning. This solution helps district heating providers optimize their operations while reducing costs and supporting the transition to more sustainable energy systems.

For more information about Vaisala Xweather Optimize, please visit: www.xweather.com/xweather-insight/optimize

About Vaisala
Vaisala is a global leader in measurement instruments and intelligence for climate action. We equip our customers with devices and data to improve resource efficiency, drive energy transition, and care for the safety and well-being of people and societies worldwide. With almost 90 years of innovation and expertise, we employ a team of over 2,300 experts committed to taking every measure for the planet. Vaisala series A shares are listed on the Nasdaq Helsinki stock exchange.

www.vaisala.com

Vaisala Xweather brings weather confidence to decision-makers in every weather-sensitive business. We combine local sensor data with advanced AI predictions to deliver precise and actionable weather insights. Vaisala Xweather transforms weather challenges into opportunities to optimize safety and efficiency while improving resilience against severe weather and climate change.

Media Contact:</strong

Panu Heikkinen, Senior Communications Specialist, Vaisala
Tel. +358 40 559 4175
panu.heikkinen@vaisala.com

SOURCE: Vaisala

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