Michael Ørsted, NIRAS: “Use your existing data, use energy label data as a baseline”

We no longer lack data to get started on CO₂ reduction. Denmark already has energy label data for virtually the entire building mass, standardized, comparable and available at scale. The task is to take as a basis what exists, use it in practice and continuously improve quality.

Michael Ørsted from NIRAS highlighted this during proprty.ai's CO₂ breakfast earlier this autumn, where the conversation was about how data can be brought into play today, without waiting for the perfect dataset.

“Don't wait for the perfect dataset. Start with the energy label as a common baseline, and continuously improve data quality combined with efforts for dynamic energy labels,” said Michael Ørsted during the event.

The energy label as a common starting point

The energy label is legally required for sale and rental and covers both residential, commercial and public buildings. This makes it the most widely used and comparable source of data on the energy state of buildings in Denmark.

The Danish Energy Agency makes both search and portfolio search available, so that you can work without address and on a large scale via Check Energy Label. The labels contain key figures on consumption and efficiency, as well as a list of economically prioritised improvement proposals, which make them suitable as a starting point for both screening and prioritisation.

“Of course, the estimated consumption in the energy labels is far from perfect. But they give us a common starting point and a way forward. The most important thing is to get started and use the data we already have,” said Michael Ørsted.

Quality is created through use

Data quality does not improve on the desktop, but in practice. Every supervisory task, every digitized workflow, and every validation across portfolios elevates the data level. Combined with basic data such as BBR, DAR and OIS, you get a robust, scalable data pipeline from day one.

“Data quality is created in everyday life, not in Excel. Every time you work with data, it gets a little better,” said Michael Ørsted.

How to get started

Establish baseline in scale. Drag energy labels for the entire portfolio via batch or multi-address search. Use them as a common zero point for KPIs and CO₂ roadmaps.

Prioritize based on potential and business case. Use the energy label's improvement suggestions to identify quick gains and targeted projects, and document effects to tenants and investors.

Incorporate learning loops into operations. As operations and supervision perform tasks, detect deviations and update data on year numbers, component condition, and actions performed. Then the data quality rises month by month.

Enrich with basic and system data. Combine the energy label with BBR, OIS, and meter or FM data to more accurately hit segments, building types, and priority lists.

Start with what exists

According to Michael Ørsted, the perfect dataset does not arise by waiting, but through consistent use. Starting with the energy label as a common baseline, working at scale and elevating quality through practice, decisions become both better, faster and easier to document.