Local weather forecasting reduces emissions from heating

Accurate weather data from microclimates

Fortum's district heating network area in Espoo, Kauniainen and Kirkkonummi has seven different microclimates. Waterways, topography, and the volume of trees and buildings are among the factors that affect microclimates. Local differences across such a wide area can be significant, particularly in winter when the sea’s impact on temperatures is emphasised. For example, in periods of high pressure, the temperature in North Espoo is several degrees colder than on the coast. Local weather stations can collect data about the microclimates and analyse the weather at a level that is much more precise compared to city-level weather data.

Weather data is also needed for teaching artificial intelligence about the different demand and weather models. The Vaisala weather stations placed in different microclimates collect the precise weather data that is used to teach artificial intelligence to produce extremely accurate local weather forecasts. The weather instruments are not a new invention, but Fortum's and Vaisala’s joint digitalisation project is the first in which several advanced weather stations producing comparable data are being used for highly accurate local forecasting.

Forecasting demand improves efficiency and reduces emissions

With more accurate weather forecasts, it’s possible to improve local demand forecasting and gain a better understanding of the heating needs of different neighbourhoods at different times. Fortum is also developing a very precise optimisation platform enabling the optimisation of the entire district heating system – from the production and distribution chain to the customers. When the right amount of heat is produced and distributed to customers at the right time, the energy efficiency in the region improves, the need for primary energy decreases, and emissions are reduced. The use of local production plants also can be decreased during peak demand, decreasing emissions locally.

The more accurate weather measurements and forecasts can be used also at the building level. Using the building’s own automation solutions and optimisation platforms can help to improve their energy efficiency – for instance, in spring when solar radiation has a significant impact on the demand for heating. The same solutions enable the buildings to participate in heat and electricity demand response and thus impact the efficiency and emissions of the entire energy system.