| 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 state-of-the-art 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 state-of-the-art 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 | Techno-Economic 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 state-of-the-art 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 state-of-the-art 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 state-of-the-art 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 Multi-Period Optimal Power Flow in Distribution GridsResearch method: Prototyping Research question: - Which methods can be used to calculate multi-period optimal power flow in distribution grids?
- How do these methods scale for different distribution grid sizes?
Possible approach: - Understand state-of-the-art models based on literature
- Formulate mathematical optimization problem
- Develop efficient Python or Julia code using state-of-the-art 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 physics-informed 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 state-of-the-art models based on literature
- Develop efficient Python or Julia code implementing one of the methods
- Compare PINN-based 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 learning-based prediction of battery aging Research method: prototyping Research question: - How to predict lithium-ion battery aging using ML models?
- How performant are ML models compared to other methods?
Possible approach: - Understand state-of-the-art 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 | R-C Models for Heat Transfer in Buildings Research method: Prototyping Research question: - How to design an easy-to-use Python library for fitting R-C-models of the heat transfer in buildings?
- How to use fitted models to simulate buildings in control environments?
Possible approach: - Understand state-of-the-art based on literature
- Develop efficient Python code to fit R-C 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 R-C Models Research method: Prototyping Research question: - How can EnergyPlus models be approximated by R-C models?
- How effective and efficient is the approach?
Possible approach: - Learn to work with EnergyPlus
- Develop efficient Python code fit R-C 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 physics-informed 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 state-of-the-art models based on literature
- Develop efficient Python or Julia code implementing one of the methods
- Compare PINN-based 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 Ultra-Short-Term Wind Power Forecasting Research method: Literature Research Research question: - Who are ultra-short-term (<1h) wind power forecasting stakeholders?
- In what applications are ultra-short-term 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 ultra-short-term 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 state-of-the-art studies?
- How can we set up a fair, easy-to-use wind power forecasting benchmarking framework?
Possible Approach: - Identifying state-of-the-art 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 prototype-based 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. non-federated 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 out-perform 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 |