Development of a household occupancy state simulation model for multi-energy load profile generation Residential buildings account for around 30% of the end energy consumption and CO2 emissions in Germany respectively. The transition to a more sustainable energy supply system involves much decentralized generation as well as a stronger coupling of sectors and energy carriers. This includes, for example, photovoltaic (PV), solar thermal and heat pumps to supply residential buildings with power and heat – in addition to decentralized storage units. In addition to the building itself, the users play a crucial role in shaping the energy demand in residential buildings. Furthermore, the large number of “prosumers” in the future energy system will mean that standard load profiles for heat and electricity are no longer appropriate. In some cases this is already the case, yet outdated standardized load profiles are sometimes employed in many energy system analysis studies. Hence a differentiated approach to analyze residential buildings is required if these diverse aspects are to be adequately considered. The objective of this Masters Thesis is to further develop a new methodology for the generation of occupancy state profiles for time periods of one year. These state profiles should then be used as the basis for the generation of consistent electricity, heat and mobility profiles for individual households. Existing load profile generation models do not specifically focus on interday dependencies of consecutive days due to the nature of the underlying time use survey data. This challenge should be addressed in this thesis by combining time-use survey data, which provide activity data for single days, with smart meter electricity household data, which cover longer time periods (~ 1 year). Furthermore, seasonal household behavioural changes should be investigated and implemented in the model. In order to consider electro mobility and its effects on the electrical demand in residential buildings, a mobility state should be introduced in addition to the states already used in existing models, so that the developed model enables the generation of consistent mobility, electricity and heat demand profiles. All in all the following steps should be covered: Literature review on occupancy state modelling for household load profile generation Analysis of time-use survey data and smart meter data (Use of cluster methods to identify similar sociodemographic groups) Comparison of existing Markov models (stochastic model) for the generation of Occupancy state profiles Development of a new occupancy state profile generator that meets the above mentioned requirements Validation of the developed model by comparing synthetically generated load profiles with smart meter data Finally, the developed methodology is critically evaluated and future work steps are defined in the outlook The Thesis can be written in German or English; in both cases a good understanding of German is required. Experience in programming, e.g. with Python, would be advantageous. For further information and to make an application please contact: Max Kleinebrahm, Email: Max.Kleinebrahm@kit.edu