| 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 | 2025-09 |
DIST_GRID_GEN_MAPS | 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
| 2025-09 |
LLM_EMS | LLM-Assisted Tariff Parsing for Local Energy Management Systems Research method: Prototyping Research question: How effectively can Large Language Models (LLMs) parse complex, multi-page industrial electricity tariff PDFs into functional Energy Management System (EMS) logic? How can this translation process be executed entirely locally to comply with legal constraints regarding cloud uploads?
Possible approach: Investigate LLM-assisted methods for parsing and extracting rules from complex tariff documents. Develop a fully local open-source pipeline that accepts a tariff PDF and generates a functional EMS configuration without data leaving the plant. Benchmark the rule extraction accuracy and performance across various types of electricity tariffs.
| - Python programming skills
| Yes | Manuel Katholnigg manuel.katholnigg@tum.de
| 2026-03 |
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).
| - Python programming skills
| Yes | Manuel Katholnigg manuel.katholnigg@tum.de
| 2026-03 |
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.
| - Python programming skills
| Yes | Manuel Katholnigg manuel.katholnigg@tum.de
| 2026-03 |
BENCH_WIND | State-of-the-Art Machine Learning and Deep Learning Models for Day-Ahead Wind Power Forecasting Research method: Prototyping Research questions: - How do advanced tree-based ensembles (e.g., XGBoost) compare against state-of-the-art deep learning architectures (e.g., Temporal Fusion Transformers) in predicting aggregate day-ahead wind farm output?
- How does computational complexity compare?
Possible approach: Establish an evaluation framework using generation data and Numerical Weather Prediction features. Train hyperparameter-tuned tree-based models (such as XGBoost) as the machine learning baselines. Implement and train multivariate deep learning sequence models (like a Temporal Fusion Transformer or an LSTM) on the exact same dataset. Evaluate determinisitc forecasts for the day-ahead horizon - Evaluate computational complexity of the models
| - Python programming skills
| Yes | Jonas Betscher jonas.betscher@tum.de | 2026-03 |
FOUND_WIND | Zero-Shot Time-Series Foundation Models for Day-Ahead Wind Power Forecasting Research method: Prototyping Research questions: Can large-scale time-series foundation models accurately predict day-ahead wind power output at the farm level without any target-specific fine-tuning ? How does the zero-shot performance of these foundation models compare to a fully supervised, site-specific XGBoost model?
Possible approach: Select 1–2 recent open-source time-series or wind-specific foundation models (such as Chronos, Lag-Llama, or WindFM). Generate zero-shot day-ahead forecasts. Fine-tune the foundation model using a small subset of the wind park's data to measure the performance gain from domain adaptation. Benchmark the zero-shot and fine-tuned results against a dedicated supervised baseline model to determine if the computational overhead of foundation models is justified.
| - Python programming skills
| Yes | Jonas Betscher jonas.betscher@tum.de | 2026-03 |
AI_WIND | AI Weather Models for Day-Ahead Wind Power Forecasting Research method: Prototyping Research questions: Do state-of-the-art AI weather models (e.g., GraphCast, FourCastNet, or Pangu-Weather) provide superior meteorological feature inputs for wind farm forecasting compared to traditional physics-based NWPs?
Possible approach: Extract localized surface wind speed, wind direction, and temperature forecasts for the target wind park's coordinates using an open-source AI weather model such as GraphCast or FourCastNet. Pair these AI-generated weather covariates with the existing historical farm-level power data. Train a standard predictive model (such as your current XGBoost setup) using the AI weather model outputs, and train an identical model using traditional NWP data. Compare the downstream day-ahead wind power forecasting errors to quantify the direct value added by using AI-driven meteorology.
| - Python programming skills
| Yes | Jonas Betscher jonas.betscher@tum.de | 2026-03 |
ESPARX_SOLAR_FC | 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 | 2025-09 |
SOLAR_POWER_FC | 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
| 2025-03 |
DEEP_NILM | 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 | 2023-09 |
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
| Yes | Christoph Goebel christoph.goebel@tum.de | 2023-09 |
LOCAL_ENERGY_TRADING | 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 | 2024-09 |
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 | Christoph Goebel christoph.goebel@tum.de | 2023-09 |
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 | Christoph Goebel christoph.goebel@tum.de
| 2023-09 |
RC_MODEL_FITTING | 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 | Christoph Goebel christoph.goebel@tum.de | 2023-09 |
| 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 | 2025-09 |
| 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 | 2025-09 |
PV_BAT_SZ | 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 | 2025-09 |
HOUSE_FLEX | 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 | 2025-09 |
| 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 | 2025-09 |
| 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 | 2025-09 |
| MARL_P2P | Multi-Agent Reinforcement Learning Approaches for P2P Energy Trading Research method: Literature review, prototyping Research question: How can multi-agent reinforcement learning approaches be designed and evaluated for peer-to-peer energy trading so as to achieve efficient decentralized coordination, scalability, and robust performance under realistic market and network assumptions?
Possible approach: Compare different MARL approaches for P2P energy trading Investigate trade-offs between coordination quality, scalability, fairness, and training stability Analyze assumptions regarding agent information, market mechanisms, and grid constraints Optionally test a simple prototype on a small simulated P2P trading setup and compare against a baseline method
| - Python programming skills
| yes | Hippolyte Robin hippolyte.robin@tum.de | 2026-03 |
| P2P_SETTL | On-Chain vs Off-Chain Settlement Architectures for P2P Energy Trading Research method: Literature review, conceptual modelling Research question: How do on-chain, off-chain, and hybrid settlement architectures compare in terms of trust, verifiability, scalability, and cost for peer-to-peer local energy markets, and under what market and regulatory conditions does each approach offer the most appropriate trade-offs?
Possible approach: - Survey existing settlement mechanisms in P2P energy trading platforms and adjacent domains (wholesale energy markets, decentralised finance) to build a taxonomy of architectural approaches
- Define a structured comparison framework covering key properties: settlement finality, auditability, transaction throughput, latency, privacy, and cost as a function of community size
- Analyse the measurement oracle problem — how off-chain meter data is anchored on-chain — and review cryptographic and governance-based approaches proposed in the literature
- Examine how settlement design interacts with penalty and reward mechanisms, and what game-theoretic implications different architectures have on participant behaviour
- Optionally map findings against an existing platform (e.g. GSY DEX) to ground the comparison in a concrete implementation reference
| - Python programming skills
| yes | Hippolyte Robin hippolyte.robin@tum.de | 2026-03 |
| MOD_GEN | White-box building model generation for Germany using OCHRE Research method: Prototyping Research question: - How can the advanced python-based energy modeling tool OCHRE be adapted to generate realistic building models for Germany?
Possible approach: | | yes | Lennart Morlock lennart.morlock@tum.de | 2026-03 |
| BAT_REV_OPT | Large-scale battery storage revenue optimization under multi-market participation Research method: Prototyping Research question: Possible approach: | | yes | Lennart Morlock lennart.morlock@tum.de | 2026-03 |