Master Thesis Data-Driven Identification and Forecasting of Thermal Flexibility in Residential Heat Pump Systems

Residential heat pumps can provide electrical flexibility by leveraging the thermal storage capacity available within the entire energy system including the building thermal mass domestic hot water (DHW) storage and where available buffer storage tanks. Bosch already has concepts for thermal characterization flexibility estimation as well as energy consumption forecasting. Your thesis should therefore focus on making these concepts operational by identifying system-specific thermal characteristics from monitoring data and using them to forecast energy consumption as well as available flexibility. The question is: how can the thermal characteristics and usable storage capacities of residential heat pump energy systems including building thermal mass DHW storage as well as buffer storage be automatically identified from monitoring data and how can this information be integrated into forecasting concepts to estimate electrical energy consumption as well as available thermal flexibility

• You will review existing Bosch modelling concepts for thermal characterization and flexibility estimation as well as analyzing empirical field monitoring data to define data pipelines input/output requirements and relevant data quality criteria.
• In addition you will automatically identify building- and storage-specific thermal parameters (e.g. thermal resistance capacitance). You will also have the option of benchmarking data-driven identification approaches against Neural Ordinary Differential Equations (Neural ODEs) in terms of parameter plausibility computational effort as well as model accuracy.
• Furthermore you will integrate the parameterized models into forecasting pipelines in order to estimate electrical load profiles and quantify time-dependent flexibility margins. You will compare forecasting as well as flexibility metrics at the level of individual buildings and aggregated fleets (including statistical analysis of the effects of aggregation).
• As part of your thesis you will develop an automated end-to-end prototype workflow covering data pre-processing model parameter identification instantiation and forecast generation. You will validate the prototype against measurement data documenting your findings as well as the systems limitations.
• During your assignment you will compare house-level and fleet-level data as well as analyze consumption patterns based on monitoring data. You may also choose to conduct a more detailed statistical analysis of aggregation methods.
• Last but not least your task will include an evaluation of the identification and forecasting workflow based on real-world monitoring data a benchmark comparison of identification methodologies (e.g. RC models vs. Neural ODEs) as well as scale (single-home vs. fleet aggregation) and concrete practical recommendations for the deployment of automated flexibility forecasting within future Bosch residential energy solutions.

Qualifications :

• Education: Master studies in the field of Computer Science or comparable
• Experience and Knowledge: of Python; experience with databases and knowledge of heat pump systems advantageous
• Personality and Working Practice: you are a reliable curious individual with a strong internal motivation and a willingness to learn new things
• Work Routine: partially mobile working is possible with work on premises at 60% (three days a week)
• Languages: business fluent in German and English

Additional Information :

Start: according to prior agreement
Duration: 6 months

Requirement for this thesis is the enrollment at university. Please attach your CV transcript of records examination regulations and if indicated a valid work and residence permit.

Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore we welcome all applications regardless of gender age disability religion ethnic origin or sexual identity.

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Ana Constantin (Functional Department)

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Remote Work :

No

Employment Type :

Full-time