Optimizing residential heating: a data-centric approach to heat pump implementation

As the world shifts towards renewable energy sources, heat pumps have emerged as a critical technology for reducing reliance on fossil fuels in building heating systems. These pumps, which draw energy from air or ground sources, represent a significant advance over traditional fossil fuel heating methods. Yet, not all buildings are currently suited for direct transition to heat pump technology; some require retrofitting to optimize energy efficiency, while others are already efficient enough to make the switch. The challenge lies in accurately identifying which houses are ready for heat pump implementation and which ones need retrofitting. Current heat systems, including gas boilers, offer a wealth of data, but lack crucial building context information like size, insulation, room temperatures or heating system hydraulic architecture. This gap in data hinders the effective analysis and optimization of heating systems. This thesis aims to develop a model using gas boiler data to deduce building context information. The goal is to identify homes ready for heat pump implementation, streamlining the transition towards more sustainable heating solutions.

Keywords: heat system analysis, heat pump, data analytics, machine learning

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Location

Bosch IoT Lab (ETHZ)

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Topics

• Information, Computing and Communication Sciences
• Engineering and Technology

Documents

Name	Comment	Size	Actions
Transition_to_heat_pump.pdf		154KB	Download