| Acronym | Topic description | Required skills | Group work possible (group of two) | Contact | Time Topic Added |
|---|
FLEX_PROD | Using Flexibility of Production Systems for Energy Management Research method: Prototyping Research questions: - How can the flexibility of production systems (e.g., running machines slower or faster) be used to improve energy management (e.g., reducing cost by using more locally generated solar power or adjusting consumption to dynamic tariffs)
- How can corresponding optimization problems be defined and solved?
- How can different optimization methods be benchmarked?
Possible approach: - Understand state-of-the-art based on literature
- Define optimization scenarios (e.g., based on production use cases described in the literature)
- Develop efficient Python or Julia code to implement an optimization method
- Benchmark optimization method using baseline scenarios
| - Python/ Julia programming skills
| No | Christoph Goebel christoph.goebel@tum.de | 09/2025 |
EMSX_EXT (NOT AVAILABLE FOR WS25/26) | Extension of the Energy Management System Benchmarking Framework EMSx Research method: Prototyping Research questions (selection possible): - How can EMSx be extended to include more sophisticated modeling capability (e.g., similar to OCHRE)?
- How can EMSx be extended to use other datasets and higher time resolution?
- How can EMSx be extended to enable benchmarking of reinforcement learning algorithms?
Possible approach: - Understand current EMSx framework and code (written in Julia)
- Develop efficient Julia code to implement selected extensions
- Evaluate extensions using actual data
| - Ideally Julia programming skills
- Basic understanding of optimization methods
| Yes | Paul Magos paul.magos@tum.de | 03/2025 |
DIST_GRID_GEN_MAPS (ALREADY TAKEN FOR WS25/26) | Map-Based Distribution Grid Model Generation Methods Research method: Prototyping Research questions: - How can realistic distribution grid models be synthesized based on map data?
- How can synthesized distribution grid models be evaluated?
Possible approach: - Understand state-of-the-art based on literature
- Develop efficient Python or Julia code to implement a distribution grid generation method
- Evaluate method using actual data
| - Python/ Julia programming skills
- Understanding of distribution grid modelling
| Yes | Christoph Goebel christoph.goebel@tum.de
| 09/2025 |
ESPARX_LOAD_FC | Using e-SparX for Developing Load Forecasting Models Research method: Prototyping Research questions: - How can e-SparX be used to develop load forecasting models and make them transparent and sharable?
- How to extend and improve e-SparX to make it more useful and usable?
Possible approach: - Understand state-of-the-art of load forecasting based on literature
- Understand current e-SparX implementation
- Develop efficient Python or Julia code to implement several load forecasting models
- Share the complete ML pipelines using e-SparX
- Propose new e-SparX features to improve its usability
- Optionally implement new e-SparX features
| - Python programming skills
| Yes | Manuel Katholnigg manuel.katholnigg@tum.de
| 09/2025 |
ESPARX_SOLAR_FC (ALREADY TAKEN FOR WS25/26) | Using e-SparX for Developing Solar Power Forecasting Models Research method: Prototyping Research questions: - How can e-SparX be used to develop solar power forecasting models and make them transparent and sharable?
- How to extend and improve e-SparX to make it more useful and usable?
Possible approach: - Understand state-of-the-art of solar power forecasting based on literature
- Understand current e-SparX implementation
- Develop efficient Python or Julia code to implement several solar power forecasting models
- Share the complete ML pipelines using e-SparX
- Propose new e-SparX features to improve its usability
- Optionally implement new e-SparX features
| - Python programming skills
| Yes | Christoph Goebel christoph.goebel@tum.de | 09/2025 |
SOLAR_POWER_FC (ALREADY TAKEN FOR WS25/26) | Probabilistic Solar PV Power Forecasting Research method: Prototyping Research questions: - How can solar PV power output be forecasted probabilistically on different time horizons (intra-hour, intra-day, day_ahead)?
- How can corresponding methods be implemented and benchmarked?
Possible approach: - Understand state-of-the-art based on literature
- Develop efficient Python or Julia code to implement probabilistic forecasting methods
- Evaluate method using actual data
| - Python/ Julia programming skills
| Yes | Christoph Goebel christoph.goebel@tum.de
| 03/2025 |
RE_FC_ECON_IMPACT (ALREADY TAKEN FOR WS25/26) | Economic Impact of Renewable Power Forecasting Error Research method: Prototyping Research questions: - How can the economic impact of renewable power forecasting errors be assessed?
- How much money can a wind power plant save by improving its forecast?
Possible approach: - Understand state-of-the-art based on literature
- Develop efficient Python or Julia code to implement assessment method
- Evaluate method using actual data
| - Python/ Julia programming skills
- Understanding of electricity markets
| Yes | Manuel Katholnigg manuel.katholnigg@tum.de
| 03/2025 |
DEEP_NILM (ALREADY TAKEN FOR WS25/26) | Deep Learning for Energy Disaggregation Research method: Prototyping Research questions: - 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
| Yes | Christoph Goebel christoph.goebel@tum.de | 09/2023 |
DEEP_HEAT (NOT AVAILABLE FOR WS25/26) | 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
| Yes | Christoph Goebel christoph.goebel@tum.de | 09/2023 |
LOW_VOLT_LOAD_FC | Probabilistic Low-Voltage Load Forecasting in Distribution Grids Research method: Prototyping Research question: - Which methods exist for forecasting low-voltage load on different time horizons (intra-hour, intra-day, day_ahead)?
- Which advantages does global forecasting provide?
- How can different models be compared fairly using public datasets?
Possible approach: - Understand state-of-the-art based on literature
- Develop efficient Python code to train models using public load datasets
- Evaluate performance by benchmarking with methods described in literature
| - Python programming skills
| No | Hippolyte Robin hippolyte.robin@tum.de
| 09/2025 |
LOCAL_ENERGY_TRADING (ALREADY TAKEN FOR WS25/26) | Local Energy Trading Under Network ConstraintsResearch method: Prototyping Research question: - How can prosumers trade energy locally without violating the physical constraints of the distribution system?
- Which approaches already exist and which assumptions are they based on?
- 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
| No | 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
| Yes | Hippolyte Robin hippolyte.robin@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
| Yes | Paul Magos paul.magos@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
| Yes | Lennart Morlock lennart.morlock@tum.de
| 09/2023 |
RC_MODEL_FITTING (NOT AVAILABLE FOR WS25/26) | Using EnergyPlus to fit R-C Models for Heat Transfer in Buildings 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
| Yes | Lennart Morlock lennart.morlock@tum.de
| 09/2023 |
| ANN_OPF | ----This topic is only available in English---- Power Grids ANN-based Optimal Power Flow Research method: Prototyping Research question: - What model design is suitable for improving ANNs optimal power flow predictions?
- What training approaches are suitable for maintain predictions feasibility and optimality?
Possible approach: - Understand state-of-the-art based on literature
- Implement ANN model for optimal power-flow prediction
- Benchmark results against SOTA ANNs
| - Programming skills in Python
| Yes | Arbel Yaniv arbel.yaniv@tum.de | 09/2025 |
| ROPF_Data | ----This topic is only available in English---- Generating Representative AC-OPF Datasets Research method: Prototyping Research question: - How to systematically generate representative AC OPF datasets that span the feasible region?
- How to measure the generated data level of representativeness?
Possible approach: - Understand state-of-the-art based on literature
- Enhance existing state-of-the-art methods to increase level of representativeness
- Benchmark against dataset generated with SOTA approaches
| - Python programming skills
| Yes | Arbel Yaniv arbel.yaniv@tum.de | 03/2025 |
| NILM_AL | ----This topic is only available in English---- Non-Intrusive Load Monitoring with Active Learning Research method: Prototyping Research question: - What is the impact of different acquisition functions on the performance of appliances disaggregation?
- What is the best fine-tuning approach, both layer-wise and sample-wise?
Possible approach: - Understand state-of-the-art based on literature
- Leverage scikit-activeml to analyse different active-learning approaches for NILM
- Try different fine-tuning experimental setups
| - Python programming skills
| Yes | Arbel Yaniv arbel.yaniv@tum.de | 09/2025 |
GNN_SE (ALREADY TAKEN FOR WS25/26) | ----This topic is only available in English---- Power Grids GNN-based State Estimation Research method: Prototyping Research question: - What training procedures potentially improve GNNs state estimation predictions?
- What architectures design potentially improve GNNs state estimation predictions?
Possible approach: - Understand state-of-the-art based on literature
- Enhance state-of-the-art GNN model for power grid state estimation
- Benchmark results against SOTA ANNs
| - Python programming skills
| yes | Arbel Yaniv arbel.yaniv@tum.de | 09/2025 |
| MARL_COOP_OPT | Cooperative Control of Energy Systems using Multi-Agent Reinforcement Learning Research method: Prototyping Research question: How can a cooperative multi-agent reinforcement learning (MARL) framework be designed to simultaneously optimize forecasting models and battery dispatch strategies? What agent configurations and reward structures are most effective for achieving system-wide economic goals? How does this cooperative MARL approach compare to decentralized or traditional, separate optimization methods?
Possible approach: Understand the state-of-the-art in MARL for energy systems and forecast-aware control based on literature. Develop a simulation environment for a system including PV/load profiles, a battery, and market prices. Implement and train a cooperative MARL algorithm where agents (e.g., a forecast agent and a battery control agent) work together. Benchmark the MARL approach against relevant baseline strategies.
| | yes | Manuel Katholnigg manuel.katholnigg@tum.de | 09/2025 |
| ECON_FC | Decision-Aware Forecasting for Optimal Energy System Operation Research method: Prototyping Research question: How can forecasting models for PV generation or electricity load be optimized for economic performance rather than purely statistical accuracy? What loss functions or training frameworks can directly incorporate the economic consequences of forecast errors? How do these economically-optimized forecasts perform in a simulated operational environment (e.g., battery scheduling) compared to standard forecasts?
Possible approach: Understand the state-of-the-art in decision-aware forecasting based on literature. Define an economic objective for a specific application (e.g., minimizing electricity costs for a household with a battery and PV system). Develop and implement a forecasting model (e.g., based on an ANN) using a custom, economically-derived loss function. Evaluate and benchmark the model's resulting economic performance against models trained with standard statistical metrics (e.g., MSE, MAE).
| | yes | Manuel Katholnigg manuel.katholnigg@tum.de | 09/2025 |
PV_BAT_SZ (ALREADY TAKEN FOR WS25/26) | PV and battery sizing with limited or low-resolution data Research method: Prototyping Research question: - How can the sizing of PV and battery storage systems be optimized when only limited or low-resolution data is available?
Possible approach: - Understanding the state of the art from the literature
- Implementation of possible approaches to improve dimensioning with limited data.
| Python | no | Elgin Kollnig elgin.kollnig@tum.de | 09/2025 |
HOUSE_FLEX (ALREADY TAKEN FOR WS25/26) | Probabilistic household flexibility estimation Research method: Literature review & Prototyping Research question: - What methods exist for determining household flexibility, particularly taking probabilistic approaches into account?
Possible approach: - Literature review on methods for determining household flexibility
- Analysis of methods for probabilistic determination of flexibility
- Implementation and comparison of different approaches
| Python | no | Elgin Kollnig elgin.kollnig@tum.de | 09/25 |
| DOPT_P2P | Distributed optimization methods for P2P energy trading Reseach method: Literature review, prototyping Research question: - How can distributed optimization methods (e.g., ADMM or consensus-based algorithms) be designed to ensure fast convergence, scalability, and network constraint satisfaction in P2P energy trading within distribution systems?
Possible approach: Compare different distributed optimization methods Investigate trade-offs between convergence speed, privacy, and accuracy Test on small IEEE distribution feeders with simulated P2P trades
| Python | no | Hippolyte Robin hippolyte.robin@tum.de | 09/25 |
| BLOCKCHAIN_P2P | Blockchain-based P2P energy trading Research method: Literature review, prototyping Research question: - What blockchain architectures, consensus mechanisms, and smart contract designs have been proposed for P2P energy trading, and how do they address challenges of scalability, security, and transaction costs?
Possible approach: - Classify blockchain architecture, noting their trade-offs in decentralization, scalability, and governance
- Compare consensus mechanisms, discuss how each handles energy efficiency, speed, security
- Review smart contracts design (how trades are matched, settled, automated)
- Identify challenges and research gaps
| Python | no | Hippolyte Robin hippolyte.robin@tum.de | 09/25 |
| LOAD_GEN | High-resolution load profile generation for Germany using OCHRE Research method: Prototyping Research question: Possible approach: | | yes | Lennart Morlock lennart.morlock@tum.de | 09/2025 |
| LOAD_ANALYS | Analyzing german load/generation datasets on the distribution grid level for open data collection Research method: Prototyping Research question: What open-source datasets are available on distribution grid level loads and generation? How could they be added to the Professorship of Energy Management Technologies' Open data collection exploratory data analysis (EDA) jupyter notebooks for better accessibility?
Possible approach: | | yes | Lennart Morlock lennart.morlock@tum.de | 09/2025 |