Acronym  Topic description  Required skills  Contact  Time Topic Added 

DEEP_NILM  Deep Learning for Energy Disaggregation Research method: Prototyping Research question:  How can Deep Learning be applied to Energy Disaggregation / NILM?
 How does Deep Learning perform compared to other methods?
Possible approach:  Understand stateoftheart based on literature
 Develop efficient Python code to train DL models on public NILM datasets
 Evaluate performance by benchmarking with methods described in literature
  Python programming skills
 Christoph Goebel christoph.goebel@tum.de  09/2023 
DEEP_HEAT  Deep Reinforcement Learning for HVAC Control Research method: Prototyping Research question:  How can Deep Reinforcement Learning be applied to HVAC Control problems?
 How does Deep Reinforcement Learning perform compared to other methods?
Possible approach:  Understand stateoftheart based on literature
 Develop efficient Python code to train DRL models using HVAC model
 Evaluate performance by benchmarking with methods described in literature
  Python programming skills
 Christoph Goebel christoph.goebel@tum.de  09/2023 
ES_EVAL  TechnoEconomic Evaluation of Combined Energy Storage Technologies in Neighborhoods Research method: Data Analysis & Modeling Research question:  How can different types of energy storage be combined in a neighborhood setting?
 How can different scenarios be compared technically and economically?
Possible approach:  Understand stateoftheart based on literature
 Develop models to understand technical limitations and economics of energy storage
 Define scenarios and investigate potential of different types of energy storage and their combinations
  Python programming skills
 Background in energy system modelling
 Christoph Goebel christoph.goebel@tum.de  04/2024 
P2P_ENERGY_TRADING  Decentralized P2P Energy Trading Under Network ConstraintsResearch method: Prototyping Research question:  How can peer to peer energy trading in distribution grids be realized while respecting physical constraints?
 Which approaches exist?
 How can these approaches be benchmarked using realistic distribution grid models?
Possible approach:  Understand stateoftheart based on literature
 Develop efficient Python or Julia code implementing existing methods
 Evaluate performance of methods using realistic models of distribution grids
  Python/ Julia programming skills
 Christoph Goebel christoph.goebel@tum.de  09/2024 
ELECROTHERMAL_DEMAND  Containerized Implementation of Integrated Electrothermal Building Energy Demand ModelResearch method: Prototyping Research question:  How can electricity and heat demand be concurrently simulated?
 How can the simulation be packaged into a Docker container and controlled via APIs?
Possible approach:  Understand stateoftheart models based on literature
 Develop efficient Python or Julia code implementing one of the methods
 Package code in a Docker container and evaluate the implementation via EMS use cases
  Python/ Julia programming skills
 Christoph Goebel christoph.goebel@tum.de  09/2024 
MULTI_PERIOD_OPF  Computing MultiPeriod Optimal Power Flow in Distribution GridsResearch method: Prototyping Research question:  Which methods can be used to calculate multiperiod optimal power flow in distribution grids?
 How do these methods scale for different distribution grid sizes?
Possible approach:  Understand stateoftheart models based on literature
 Formulate mathematical optimization problem
 Develop efficient Python or Julia code using stateoftheart methods to solve the problem
 Evaluate solution method using realistic distribution grids with solar, load, and storage
  Python/ Julia programming skills
 Christoph Goebel christoph.goebel@tum.de  09/2024 
PINN_LOADFLOW  Research method: prototyping Research question:  How can the method of physicsinformed NNs be used to calculate load flows in power distribution systems?
 How do these methods perform compared to standard numerical optimization methods?
Possible approach:  Understand stateoftheart models based on literature
 Develop efficient Python or Julia code implementing one of the methods
 Compare PINNbased approach to numerical optimization based on quality and solving speed
  Python/ Julia programming skills
 First experience with deep learning
 Christoph Goebel christoph.goebel@tum.de  09/2024 
BAT_AGE_PRED  Machine learningbased prediction of battery aging Research method: prototyping Research question:  How to predict lithiumion battery aging using ML models?
 How performant are ML models compared to other methods?
Possible approach:  Understand stateoftheart based on literature
 Develop an efficient Python program that fits various ML models on battery aging data
 Benchmarking of the developed models
  Python/ Julia programming skills
 First experience with deep learning
 Christoph Goebel christoph.goebel@tum.de  09/2023 
RC_MODELS  RC Models for Heat Transfer in Buildings Research method: Prototyping Research question:  How to design an easytouse Python library for fitting RCmodels of the heat transfer in buildings?
 How to use fitted models to simulate buildings in control environments?
Possible approach:  Understand stateoftheart based on literature
 Develop efficient Python code to fit RC models
 Evaluate implementation, e.g., by benchmarking results and evaluating computational complexity
  Python/ Julia programming skills
 Christoph Goebel christoph.goebel@tum.de  09/2023 
RC_MODEL_FITTING  Using EnergyPlus to fit RC Models Research method: Prototyping Research question:  How can EnergyPlus models be approximated by RC models?
 How effective and efficient is the approach?
Possible approach:  Learn to work with EnergyPlus
 Develop efficient Python code fit RC models to output data
 Benchmark method
  Python/ Julia programming skills
 Christoph Goebel christoph.goebel@tum.de  09/2023 
PINN_STATE_ESTIMATION  Research method: Prototyping Research question:  How can the method of physicsinformed NNs be used to predict the state of power distribution systems?
 How do these methods perform compared to standard numerical optimization methods?
Possible approach:  Understand stateoftheart models based on literature
 Develop efficient Python or Julia code implementing one of the methods
 Compare PINNbased approach to numerical optimization based on quality and solving speed
  Python/ Julia programming skills
 First experience with deep learning
 Christoph Goebel christoph.goebel@tum.de
 09/2024 
WPF_USECASES  Identifying Use Cases for UltraShortTerm Wind Power Forecasting Research method: Literature Research Research question:  Who are ultrashortterm (<1h) wind power forecasting stakeholders?
 In what applications are ultrashortterm wind power forecasts required?
 How can the benefit of improving wind power forecasts in these applications be quantified?
Possible approach:  Thorough literature research on, e.g., energy markets, trading wind energy, operating wind power plants, etc.
 Identification and characterization of practical use cases for ultrashortterm wind power forecasting.
 Propose of a quantification method for the value of wind power forecasts in two to three of your identified use cases.
  Curiosity in wind energy and energy markets
 Structured way of working
 Annika Schneider a.k.schneider@tum.de  09/2024 
WPF_BENCH  Benchmarking Wind Power Forecasting Methods Research Method: Literature Review (& Prototyping) Research Question:  How are wind power forecasting models benchmarked in stateoftheart studies?
 How can we set up a fair, easytouse wind power forecasting benchmarking framework?
Possible Approach:  Identifying stateoftheart wind power forecasting models
 Identifying today's approaches on how to benchmark new wind power forecasting models
 Proposal (and implementation) of a wind power forecasting benchmarking framework
  Curiosity in wind energy
 High interest in deep learning time series forecasting methods
 Structured way of working
 Optional: basic Python skills
 Annika Schneider a.k.schneider@tum.de  09/2024 
COMP_ENERGY  Evaluating the Energy Impact of Computation for Machine Learning Research method: Literature Review / Prototyping Research question: Possible approach: Read and review papers on the environmental impacts of Machine Learning Provide summaries of methods and develop guidelines on the types of ML model design, configurations, parameters, or algorithms that lead to lower energy consumption  Look for popular power estimation libraries from the literature and on GitHub
 (Stretch) Use one of these libraries to benchmark state of the art ML techniques and document the results
 Interest in Computational Energy Interest in ML and computer architectures Basic Python skills  Ehimare Okoyomon e.okoyomon@tum.de  04/2024 
IMPL_HP  Implementation of a Paper with Heat Pump Model and Thermal Storage Research method: Prototyping
Research question:  How can MILP be used in practice to optimize the design and operation of heat pumps?
 Can the results of the paper be reproduced with public data?
Possible approach:   Programming skills in Python
 Knowledge of linear optimization helpful
 Elgin Kollnig elgin.kollnig@tum.de
Simon Zollner simon.zollner@tum.de
 04/2024 
IMPL_AGING  Implementation of various Battery Ageing Models in an Energy Management System Research method: Prototyping
Research question:  How can different battery ageing models be compared fairly?
 How can approaches from the literature be implemented and compared with each other in a prototypebased and reproducible way?
Possible approach:   Programming skills in Python
 Knowledge of linear optimization
 Interest in batteries
 Elgin Kollnig elgin.kollnig@tum.de
Simon Zollner simon.zollner@tum.de
 04/2024 
GRID_TARIFF  Comparison of the influence of different variable grid tariffs Research method: Literature review Research question:  What options are there for designing variable grid tariffs?
 What effects do the different variable grid tariffs have on grid utilization and profitability for end consumers?
Possible approach:  Identification of different variable grid tariffs
 Identification of the advantages and disadvantages of the different grid tariffs
 (Development of new options for variable grid tariffs)

 Elgin Kollnig elgin.kollnig@tum.de  09/2024 
FED_HOU  Federated machine learning for household load forecasting Research method: Prototyping Research question:  Can we forecast household load without seeing a household's sensitive data?
 Which forecasting models are suitable for a federated setting?
Possible approach:  Literature review on federated forecasting
 Find & preprocess suitable datasets
 Build federated forecasting pipeline
 Compare results vs. nonfederated forecasting methods
  Programming skills in Python
 Basic statistics/ML knowledge
 Jan Marco Ruiz de Vargas janmarco.ruiz@tum.de
 04/2024 
COR_ENCR  Fast computation of correlation measures with homomorphic encryption Research method: modeling Research question:  Can two parties owning a data vector compute a correlation measure together, without revealing their private data to each other?
 Can we devise a (partial/ fully) homomorphic encryption scheme to calculate various correlation measures?
 How fast can this scheme be?
Possible approach:  Understand homomorphic encryption
 Understand correlation measures
 Model communication & computation protocol

 Jan Marco Ruiz de Vargas janmarco.ruiz@tum.de  09/2024 
SINDY_BAT  SINDy (Sparse Identification of Nonlinear Dynamical Systems)based Battery Aging Model Research method: Prototyping Research question:  How to implement SINDy to formulate battery aging problem?
 How to design the a feature library theta for SINDy model, and how different could the results be?
 How does the SINDy model perform comparing to other battery aging model (e.g. NNs / other symbolic regression model etc..)?
Possible approach:  Literature review on SINDy and battery aging prediction
 Implement SINDy model and train with existing dataset
 Compare the results with the existing battery aging model.
  Programming skills in Python
 Basic ML and/or Battery aging knowledge
 Sheng Yin sheng.yin@tum.de  09/2024 
MARL_EMS  Multi Agent Reinforcement Learning (MARL) for Power Split Control Research method: Prototyping Research question:  How can MARL solve power split control problem?
 Can MARL outperform centralized RL?
Possible approach:  Literature review on MARL and RL in power split control
 Build RL and MARL environment for Power split Control
 Implement Basic RL and MARL algorithm to compare the results
  Programming skills in Python
 Basic ML and/or RL knowledge
 Sheng Yin sheng.yin@tum.de  09/2024 