Advancing competence for the development of model-based data mining analysis methods in the field of building technology

An essential element of European socio-political strategy is the development of a sustainable, independent energy supply. This objective can only be reached through the continued expansion of regenerative energy systems and an intelligent network between energy provision, energy storage and energy distribution. In this context, building technology is a key discipline due to the unchanged high portion of energy demand for heating and air conditioning. Therefore, the building of the future will have to be able to take on tasks related to energy production, as well as short- and long-term energy storage. Solution strategies based on coordinated and comprehensively optimized overall system designs using individual buildings or building units as energy storage constitute a promising alternative. Net energy storage concepts such as power-to-heat and power-to-cool technologies offer more flexibility for future energy supply systems (demand side management). This type of energy management requires precise knowledge regarding buildings’ load behavior and the utilization of possible storage potentials always considering individuals’ comfort and process requirements.

Fig. 1: Potential market volumne and costs of sensors in the future (Yole, 2014)

Project idea

Massive price reductions in the field of sensor technology (see Fig. 1) and wireless data transmission, as well as the promotion of IoT (Internet of Things) and IoS (Internet of Sensors), enable new, cost-efficient possibilities in the near future to control relevant variables and provide this data in digital form. This allows for new possibilities in model-based data analysis and the system identification and modelling based on affordable sensors and sensor networks, respectively. 

Fig. 2: Areas of application for new sensors in Demand Side Management


Reasonable use of these technologies requires new analysis methods that allow for the intelligent interpretation of measured data. In addition to statistical methods, model-based methods are expected to come into play. These methods offer a high degree of flexibility regarding the assessment and interpretation options in order to generate added value from the data made available and to provide this information in a goal-oriented manner to support demand side management. The development of such analytics requires information about the current thermic conditions of a building and its expected future energy demand. It is the aim of the project on hand to build and establish the competences and infrastructure in the form of a high-performing research group.

Yole (2014): MEMS Report 2014, Yole Developpment, 69100 Lyon-Villeurbanne, Frankreich

Funding agencies, research & cooperation partners

Funding agency

This project is funded by the programme COIN by BMWFW.

Project team and project manager

Project manager

Project team

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