| Acronym | Topic description | Required skills | Contact |
|---|
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 |
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 |
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 |
DEEP_WIND | Deep Learning for Wind Power Prediction Research method: Prototyping Research question: - How can Deep Learning be applied to Wind Power Forecasting?
- 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 datasets
- Evaluate performance by benchmarking with methods described in literature
| - Python programming skills
- First experience using deep learning
| Christoph Goebel christoph.goebel@tum.de |
LEM_GRID_CONSTRAINTS | Impact of Local Energy Markets on Distribution Grid Stability Research method: Prototyping Research question: - How can the impact of Local Energy Markets on distribution grid stability be measured?
- Which mechanisms could be introduced to prevent stability issues?
Possible approach: - Understand state of the art based on literature
- Develop efficient Python code to simulate simple LEM scenarios together with distribution grid effects
- Develop basic mechanisms to prevent stability issues and evaluate using LEM/DG simulation
| - Python programming skills
| Christoph Goebel christoph.goebel@tum.de |
DL_DG_ESTIMATION | Deep Learning based Estimation of Distribution Grid State Research method: Prototyping Research question: - How can state of distribution grids be estimated using deep learning?
Possible approach: - Understand state of the art based on literature
- Develop efficient Python applying deep learning to estimate distribution grid states
- Benchmark with state of the art methods
| - Python programming skills
- First experience using deep learning
| Christoph Goebel christoph.goebel@tum.de |
DYN_BAT_AGE_PRED_CONTROL | Integration of a dynamic aging prediction for Li-ion batteries in EMS control Research method: prototyping Research question: - How can the aging process of Li-ion batteries be modeled for use in control?
- How can such models be integrated into dynamic controllers?
Possible approach: - Understand the state of research based on literature
- Development of an efficient Python program that uses a battery aging models for control
- Benchmarking of the developed method
| - Python programming skills
| Christoph Goebel christoph.goebel@tum.de |
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 programming skills
| Christoph Goebel christoph.goebel@tum.de |
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 programming skills
| Christoph Goebel christoph.goebel@tum.de |
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 programming skills
| Christoph Goebel christoph.goebel@tum.de |
PV_AGING | Age-Conscious System Efficiency Modeling of a Photovoltaic System Research method: Prototyping Research question: - How can the efficiency of a PV system on the institute's roof be modeled based on measured data and weather conditions?
- What conclusions about the age of the system can be drawn based on this modeling?
Possible approach: - Literature review on modeling efficiencies of real PV systems, with a focus on aging.
- Calculation of efficiency trends based on measured time series and logged weather data from the region.
- Analysis of efficiency trends with regard to system aging and nominal data of the components.
| Basic Python programming skills | Sebastian Eichhorn
sebastian.eichhorn@tum.de |
LAB_PSIM | Analysis of Stability Issues in Load Simulators Research method: Literature Research Research question: Possible approach: Literature review on various load simulation methods in Power-Hardware-in-the-Loop (PHIL) laboratories. Literature review on known instabilities in the technologies used OR analysis of the researched components for potential instability using minimal models. Evaluation of the results.
| Profound understanding of system stability concepts and power electronic systems | Sebastian Eichhorn
sebastian.eichhorn@tum.de |
FMI_RT | Real-time Simulation of Functional Mockup Units
Research method: Prototyping Research question: - How can Functional Mockup Units (FMU) be best integrated into a real-time simulation framework (HELICS)?
- How can a scalable simulation environment be easily set up?
Possible approach: Research on FMU and HELICS as well as real-time simulation of energy systems Definition of the programming language to be used and setup of the interface between FMU (which is available) and HELICS, as well as the scalable HELICS setup Functional test and execution of exemplary real-time simulation
| Basic Python or Julia programming skills | Sebastian Eichhorn
sebastian.eichhorn@tum.de |
HH_Control | Controllable Household Models
Research method: Prototyping Research question: - How can households, and in particular the control options that an energy management system (EMS) has, be modeled?
- How can various intervention options be modeled within a model without needing to adapt the model for a new application?
- How an the model be structured to incorporate individual detailed models later on(e.g. heat pump models)?
Possible approach: Research on smart households and intervention options of EMS Development of a control concept using real-life EMS as an example Programming of the operational model and representation of standard scenarios
| Basic Python or Julia programming skills | Sebastian Eichhorn
sebastian.eichhorn@tum.de |
EMS_COMP | Comparison of Open-Source Energy Management System (EMS) Research method: Literature Research Research question: - What are the (new) open-source Energy Management System (EMS) frameworks available?
- How do they differ, and which frameworks are suitable for specific use cases?
Possible approach: Literature review on various EMS frameworks. Analysis of the different advantages and disadvantages of the systems. Compilation of a concise guide indicating which system is suitable for each specific application.
| Profound understanding of energy management systems | Sebastian Eichhorn
sebastian.eichhorn@tum.de |
DIST_LEMLAB | Distributed Implementation of lemlab Background: lemlab is a simulation tool for local energy markets, code if open-source Research method: Prototyping Research question: - How can lemlab be implemented in a scalable way using state of the art cluster technology?
Possible approach: | Good Python programming skills, knowledge of Docker and ideally Kubernetes | Christoph Goebel christoph.goebel@tum.de
|
COMP_HH_ENERGY_SIM | Containerization and Comparison of Household Energy Demand Simulation Tools Research method: Prototyping Research question: - How can different tools for household energy demand simulation be containerized for further use in distributed environments?
- How can they be compared in terms of accuracy and computational performance?
Possible approach: | Python programming skills, knowledge of Docker | Christoph Goebel christoph.goebel@tum.de |
| DATA_PLATFORM | Data Platforms an their Way to Success Research method: Literature Research In the energy domain, there are several open data platforms, but none of it seems to be highly successful and actually attract a large user base. In other domains, platforms like "Hugging Face" celebrate huge success and truly accelerate research in their field. Research question: - Which data/code platforms are the most successful?
- How did these platforms evolve?
- How did they become succesful?
Possible approach: - Thorough literature research on the most successful platforms and their stories
- Identification of "platform success strategies"
| - Curiosity about platform success storiess
- Structured way of working
| Annika Schneider a.k.schneider@tum.de |
| LDM | TUM-EMT x Leibniz - Will the Leibniz Data Manager revolutionize our Research Data Management? Research Method: Literature Review & Prototyping Research Question: - What are the features of the Leibniz Data Manager?
- What are its limitations?
- How can the potential of the Leibniz Data Manager be measured and evaluated?
Possible Approach: - Literature review on research data management
- Testing and evaluating the Leibniz Data Manager
| - Passion for data
- Structured way of working
| Annika Schneider a.k.schneider@tum.de |
ML_Powerflow | Machine Learning based Load Flow Calculation Research method:Literature Review Research question: Which innovative solutions evolved recently to calculate load flow calculations using Machine Learning? How can those solutions be compared, which strengths and weaknesses do they have?
Possible approach: Research on machine learning technologies for load flow calculation, including most recent advancements in Graph Neural Networks (GNN) Identify strengths and weaknesses of different technologies Compare them focusing on precision and computational efficiency
| Passion for literature analysis and power system calculation | Sebastian Eichhorn
sebastian.eichhorn@tum.de |
PINN_Sim | Physics Informed Neural Networks (PINN) for Transient Power Grid Simulations Research method: Literature Review/Prototyping Research question: Possible approach: Research on PINNs and publications regarding challenges in transient simulations Setup a little prototype using public repositories provided through publications Compare the prototype with conventional solvers focusing on precision and computational efficiency
| Profound understanding of Machine Learning Technologies, Power System Simulation and advanced Python coding | Sebastian Eichhorn
sebastian.eichhorn@tum.de |
EMS_COMM | Evaluating the Next-Generation of EMS Communication Research method: Literature Review Research question: Possible approach: - Literature review on the issues related to older communication standards for EMS (Modbus, MQTT, HTTP, OCPP for electric vehicles, etc)
- Literature review on the EEBUS and Matter standards
- Provide summaries of their design, benefits, limitations, and use cases
- Research products and organizations that already use each method
- Develop guidelines for when to use which method
| Interest in Energy Management Systems, IoT, or Distributed Energy Resources | Ehimare Okoyomon
e.okoyomon@tum.de |
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 |
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
|
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
|
COMP_LOAD | Comparison of different methods for household load forecasting Research method: Literature review/Prototyping Research question: - How do different load forecasting methods differ in terms of their accuracy and applicability?
- How do the runtimes of the individual methods differ?
Possible approach: - Identification of different approaches to load forecasting
- Selection and implementation of different methods
- Evaluation of the accuracy of the different methods using suitable metrics
- Comparison of the selected methods with naive benchmarks
| - Programming skills in Python
- Interest in forecasting
| Elgin Kollnig
elgin.kollnig@tum.de |
FLEXI_QUANT | Quantification of flexibility Research method: Literature review Research question: - How can the flexibility of households be quantified?
- Which factors have an influence on flexibility?
Possible approach: - Identification of relevant literature
- Comparison of different quantification approaches for flexibility
- Identification of possible influencing factors
- (Development of new methods for quantification)
|
| Elgin Kollnig
elgin.kollnig@tum.de |
FLEXI_PRICE | Pricing of flexibility Research method: Literature review Research question: - How can the flexibility provided by households be priced?
- What parallels can be drawn with reserve markets?
Possible approach: - Identification of different pricing options for flexibility
- Identification of the advantages and disadvantages of the individual pricing options
- (Development of new flexibility pricing options)
|
| Elgin Kollnig
elgin.kollnig@tum.de |
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
|
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