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Energy Flexibility Framework

· 2 min read
Lasse Bækdal Ahrendsen
Lasse Bækdal Ahrendsen
Aalborg University, Denmark

The UPCHANGE project has reached an important milestone with the completion of Deliverable D1.2 – Energy Flexibility and Resilience Framework

This research tackles a common gap: there is no structured, data-driven way to identify which district-heating connected buildings are best suited for demand-side management (DSM) at scale. The approach combines large-scale datasets with stakeholder input (utility companies and building owners) to reflect context-specific requirements.

The framework draws on three main data sources:​

  • Smart Heat Meter (SHM): In Denmark, utility companies typically collect hourly SHM data. This usually includes cumulative heat use, flow, and supply/return temperatures, enabling detailed insight into a building’s daily energy use profiles.

  • Building and Dwelling Register (BBR): A publicly accessible national register with comprehensive information on buildings, dwellings, commercial units, and technical installations across Denmark.

  • Energy Performance Certificate (EPC): Mandatory for private buildings at construction, sale, or rental, and for public buildings. Danish EPCs are stored in a central register with public access to both inputs and outputs (including the energy label), enabling component-level analysis for certified buildings.

These datasets provide the basis for defining key performance indicators (KPIs) to support building selection, with a particular focus on thermal characteristics. For instance, BBR can supply construction year, while EPC data can be used to derive metrics such as an overall heat-loss coefficient.

An interactive data-visualisation interface supports transparent, traceable selection of candidate buildings, while keeping detailed information accessible throughout:

  1. Users can choose a geographical area and time range,

  2. Filter buildings based on defined KPIs using an interactive parallel coordinates plot (PCP) alongside a sortable table, and,

  3. Explore time-series behaviour for selected buildings – for example, aggregated 24-hour average profiles – together with associated meter and building information

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