D5.3 – Web-app tool for proactive building integration in DH networks

In recent years, the amount of data generated in buildings and their energy systems has increased dramatically. However, merely having access to large amounts of data is not a guarantee of improved performance or correct decision-making towards energy efficiency. Therefore, to derive valuable insights from data, it is essential to develop tools that can effectively analyze data and visualize key metrics to provide a higher level of understanding of the building and systems’ performance.
This document presents the development process (methodologies and algorithms), features, and architecture of the PRELUDE District Heating Meter Data Analysis web-app. This web-app has been created to help the building owners and district heating utility companies analyze the data collected by heat meters and thus assess the performance of buildings connected to the district heating network and identify faulty systems or building operations.
Several features of the PRELUDE District Heating Meter Data Analysis web-app have been implemented. They are based on data analysis methods and algorithms elaborated and validated within the PRELUDE project in the scope of WP 5 (Task 5.3) and published in peer-reviewed scientific articles (Schaffer, Tvedebrink and Marszal-Pomianowska, 2022; Leiria et al., 2022; Leiria et al., 2023). These methods are related to the following topics:
- Algorithm for disaggregation of heating energy usage profile for space heating and domestic hot water production from the total heating energy use.
- Overview of the different key performance indicators of multiple buildings connected to the district heating grid on an interactive map.
- Overview of the different key performance indicators of an individual building regarding its heating usage.
- Fault detection in the heating installations of buildings connected to the district heating network.
- Support building and heating systems energy performance analysis and decision-making.
The further development of the tool, tuning, and implementation of additional analysis methods is foreseen in the scope of WP 7, where District Heating data and specific cases will be used as demonstration cases. The tool is applied to datasets of the Aalborg district heating network to inform the utility company about possible problematic situations and faults detected within their client portfolio. The continuous improvement of the web-app tool aims to automatically diagnose faulty buildings through machine learning techniques and interoperability with the FusiX platform. The tool can be used alone or in conjunction with other FusiX applications. An API will be implemented to enable the direct use of the analysis algorithms implemented in the web-app.