Due to microclimates within the city, air temperatures can vary significantly, even over short distances. The graph demonstrates air temperature data from two -> three hyperlocal identical weather sensors only 7 km apart in the city of Espoo, Finland
Generally, surface-based networks are built to serve the gen- eral public and transportation infrastructure (e.g., airports and harbours). For this reason, they do not capture all the local weather patterns that are levant to a particular district heating network. Yet, understanding those patterns is the key to effi- cient weather forecasting in the context of district heating op- erations. Even within a mid-sized city, outdoor temperatures can vary drastically from one location to another. A city cen- tre with a densely built environment can, for example, have a different microclimate compared to a nearby district located either close to a body of water, higher above the sea level, or in a valley. The temperature difference within just several hun- dred meters can vary by several degrees. This is where accu- rate, hyperlocal weather forecasts can help. A data-driven heat production plan starts with a correct prediction. Wx Beacon by Vasiala is an enhanced hyperlocal weather fore- cast that measures local conditions in the areas of customer interest to ensure the best possible accuracy. It carefully con- siders important local topography factors such as building environment, water systems, and vegetation in various parts of the district heating network. Local measurements (space- proof technology) are combined with an in-house forecasting model (top-ranked globally), using AI/ML technologies, im- proving the city’s regional accuracy of the weather forecast. “Let’s look at the example of Fortum’s network in Espoo, Fin- land. The graph above demonstrates how significantly temperature can vary between measurement points within a relatively small area. Adding hyperlocal Wx Beacon forecast enhanced with sensor observations decreased the number of significant er- rors (over 2.5°C) by 74% and improved overall accuracy by up to 36%, helping minimize heat loss and unnecessary emissions.
With regards to CO2, monetary, and resource-saving, every net- work differs. To contextualize this, we can distinguish several different aspects in which more accurate local weather can benefit DHC companies:
Help to avoid initiating fossil plants and instead operate with a greener heat portfolio.
Allow lower water temperature. For example, a 10°C de- crease in water temperature is estimated to lead to 8.5% reduced heat losses*. Minimize situations where CHP is driven by the electric- ity-price-first approach, not the heat-demand-first ap- proach (heat to scrap). Help to make better decisions on spot markets. With CHP peak electricity production of 50MW, estimation of 150k€ yearly savings.
Save electricity thanks to optimal water pumping.
*2021, Ikävalko, Master’s Thesis
For further information please contact: Tuukka Teppola tuukka.teppola@vaisala.com
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