Description of the company
Buildings account for about 30% of the energy consumed globally. In Germany, non-residential buildings account for 37% of building-related energy consumption, in which heat consumption comprises the largest share with more than 70%. Therefore, tackling the issue of heat consumption constitutes an essential pillar in achieving a sustainable energy transition.
vilisto’s digital heat management solution transforms the non-residential building sector, by saving customers up to 40% heating energy. The self-learning radiator thermostats from vilisto comprise of integrated presence detection, learning algorithms and room climate sensors, enabling a fully automated and demand-based regulation of room temperatures. In addition, the connected web portal allows for an effective and efficient management of property portfolios. Therefore, vilisto demonstrates a viable solution for sustainably transforming building-related energy consumption by significantly reducing CO2 emissions as well as heating costs.
Situation
vilisto's radiator thermostats are the basis for digital heat management. Based on the data collected by the IoT devices, insights into room use, heating behavior and energy consumption can be generated.
Problem
The collected data can be further used to enable further added value for building management, e.g. the use of the rooms can be optimised on the basis of room utilisation behaviour. One of the challenges in providing this added value is to be cost-effective so that no major investment is required.
An interesting value in all buildings where people work is air quality. When air quality is poor, employee productivity drops dramatically without people in the room noticing that the air is getting worse over time. CO2 sensors are usually placed in the room to measure air quality. These sensors are associated with high costs and constant power consumption.
Aims of the project
Within the scope of the project, the possibilities of integrating sensor technology into the existing thermostat device connected to the radiator heating system are to be evaluated. Since CO2 sensors are very energy-intensive, a VOC sensor with low power consumption should be used to measure a CO2 equivalent. Challenges among many are temperature dependency, poor airflow, non-optimal positioning in the room, power consumption, costs, data quality.
Scopes
The following steps must be taken for the project:
Target group (students)
Mechatronics, Electrical Engineering, Environmental Engineering, Computer Science, Computational Science and Engineering and adjacent studies.
Dates
Please reserve these dates: Fishing for Experience Termine
Registration
You can apply here for Fishing for Experience.