District Heating Planning

CKW plans the most rewarding district heating networks

Anchor 1

CKW needed to know which areas to prioritize for a new district heating network (DHN) deployment.
Today, this activity involves a lot of manual effort: collecting building attributes from spreadsheets, applying complex formulas for estimating demands and mapping this data to footprints in a GIS tool for visualization and prospection purposes.
With Urbio, the full process was automated, hours of work were saved, and the team could focus on the exploration of scenarios with the support of maps and key metrics.

Customer

CKW

Sector

Utility

Location

Switzerland

Challenge

CKW approached Urbio to overcome 3 issues:

Lack of data on buildings and areas of interest.
Changing environment requiring multiple repetitions of analyses
Time-consuming methodology for evaluating multiple scenarios

Benefits

5x faster analysis enabled by instant map generation and computation of metrics

ROI achieved faster by sorting out and focusing on high-impact buildings
3x more scenarios evaluated within area of interest for feasibility studies
High data accuracy guaranteed by relying on certified industry norms and enhanced by Machine Learning and energy models
Streamlined workflow from data import to visualization of building data, to calculation of key project metrics

Solution

CKW needed to know which areas to prioritize for a new district heating network

(DHN) deployment. Today, this activity involves a lot of manual effort: collecting building attributes from spreadsheets, applying complex formulas for estimating demands and mapping this data to footprints in a GIS tool for visualization and prospection purposes. With Urbio, the full process was automated, hours of work were saved, and the team could focus on the exploration of scenarios with the support of maps and key metrics.

Centralize available data

Relying on Urbio's flexible import features, CKW quickly uploaded all files collected from different sources (municipality, utility and surveys) into the platform.
Next, they structured this data into a unique digital Twin making up for gaps in data thanks to Urbio's curated list of open data and advanced models.

Identify most suited areas

From the Digital Twin, the engineers generated multiple maps of the area highlighting the energy systems, the building uses and heat density.
Then, they applied filters to pinpoint locations of key buildings (e.g. public buildings, waste-heat producers, major heat consumers) to delineate suitable areas.

Centralize available data

Relying on Urbio’s flexible import features, CKW quickly uploaded all files collected from different sources (municipality, utility and surveys) into the platform.
Next, they structured this data into a unique Digital Twin making up for gaps in data thanks to Urbio’s curated list of open data and advanced models.