Acronym  Topic description  Required skills  Contact 

SPATIO_TEMPORAL  Does Location Matter?
Research method: Literature Research Wind power forecasting is a vast research field, where hundreds of physical, statistical and AI methods were developed over the last decades. Some methods are based on temporal information only, while other also take the location of wind power sites into account. Research question:  Does spatial information actually add a benefit to forecasting performance?
 What are other advantages and disadvantages to add spatial information to a forecasting model?
Possible approach:  Thorough literature research on the bestperforming (1) temporal and (2) spatiotemporal techniques for wind power forecasting.
 Provide an overview and analysis of their performance, advantages and disadvantages.
 Try to draw a global conclusion of whether location matters.
  Passion for structured working
 Interested in time series forecasting
 Annika Schneider annikakristina.schneider@tum.de

FLOW_NRG  FlowBased Generators in the energy context Research method: Literature Review Research question:  For which tasks in the energy context can flowbased generators be applied?
 How can the flowbased generator architecture be exploited/adopted for these specific tasks?
Possible approach:  Learn about flowbased generators and identify their strengths, weaknesses and typical applications
 Find existing studies of flowbased generators in the energy context
 Identify appropriate tasks in the energy context for these networks
 Identify a way to use/adopt these networks for these specific tasks
  Interest in modern machine learning methods
 Annika Schneider annikakristina.schneider@tum.de

GFM  Global Forecasting Methods for Joint Wind Power Forecasts Research method: Modeling Research question:  How can Global Forecasting Methods (e.g., lightGBM) be leveraged for wind power predictions of nearby wind power plants?
Possible approach:  Identification of approved global forecasting methods
 Identification of suitable data sets
 Apply method to data sets
 Evaluate results
  Programming skills in Python
 Interest in time series data and forecasting
 Annika Schneider annikakristina.schneider@tum.de

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 
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 
HYDROGEN_EVAL  Evaluation of Residential Hydrogen Storage Research method: Data Analysis & Modeling Research question:  How can hydrogen storage be used in a residential setting?
 How does it compare technically and economically to battery energy storage?
Possible approach:  Understand stateoftheart based on literature
 Develop models to understand technical limitations and economics of hydrogen storage
 Define scenarios and investigate potential of hydrogen storage compared to other technologies, in particular battery energy storage
  Python programming skills
 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 Liion batteries in EMS control Research method: prototyping Research question:  How can the aging process of Liion 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 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 programming skills
 Christoph Goebel christoph.goebel@tum.de 
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 programming skills
 Christoph Goebel christoph.goebel@tum.de 
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 programming skills
 Christoph Goebel christoph.goebel@tum.de 
EMS_BEN  Benchmarking for Energy Management Systems (EMS) Research method: Literature Review Research question: What are the targets fo benchmarking systems for EMS? Which benchmarking systems exist for EMS? What are the comparative advantages and disadvantages of them?
Possible approach:  Basic understanding of EMS  Sebastian Eichhorn sebastian.eichhorn@tum.de 
EV_CYBER  Modeling of Cyber Attacks on EV Charging Stations Research method: Prototyping Research question: Possible approach: Research on various attack scenarios and potential electrotechnical impacts Selection and definition of at least 3 realistic scenarios Modeling of these scenarios in Python (focus on electrotechnical impacts, not IT)
 Basic Python programming skills  Sebastian Eichhorn sebastian.eichhorn@tum.de 
FMI_RT  Realtime Simulation of Functional Mockup Units
Research method: Prototyping Research question:  How can Functional Mockup Units (FMU) be best integrated into a realtime simulation framework (HELICS)?
 How can a scalable simulation environment be easily set up?
Possible approach: Research on FMU and HELICS as well as realtime 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 realtime 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 reallife 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 
DIST_LEMLAB  Distributed Implementation of lemlab Background: lemlab is a simulation tool for local energy markets, code if opensource 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 
EMS_SEC  Security Analysis of Current Energy Management Systems (EMS) Research method: Literature Review Research question: What current attacks are possible on an EMS and EMS components (i.e. smart meter, PV, battery storage system, EV)?  What does the threat model looks like? What capabilities does an attacker need to have?
What mitigations are most commonly used in communication protocols, authentication, detection, etc? How big/impactful is the security risk?
Possible approach:  Basic understanding of EMS Interest in system and data security  Ehimare Okoyomon e.okoyomon@tum.de 
EMS_COST  Cost Calculations in Energy Management Systems (EMS) Research method: Prototyping Research question: How do we integrate cost calculations in OpenEMS simulations? How to account for energy bought from and sold to the grid? Can we integrate both fixed and timeofuse tariffs? Can we demo this in a real world situation? (ex. selfconsumption maximization, or export limit)
Possible approach: Develop a cost module in OpenEMS that tracks the flow of energy Add a simulator for a different price profiles (read from csv files) Add the current price and a running cost calculation to the debugger log and UI
 Basic understanding of EMS Good programming skills, java is a plus  Ehimare Okoyomon e.okoyomon@tum.de 
RL_PFC  Reinforcement Learning in a Household Setting (with Probabilistic Forecasts)
Type of research: Prototyping Research question: Method: Literature:   Knowledge in highlevel programming language
 Interest in Reinforcement Learning and Optimization
 Simon Zollner simon.zollner@tum.de 
CASE_STUDY_PUMPED  Case Study Pumped Storage
Type of research: Modelling, Prototyping Research question: Method:   basic programming skills
 Interest in linear optimization in modelling
 Simon Zollner simon.zollner@tum.de

NWP_SOLAR  Literature Review NWPbased Solar Forecasting
Type of research: literature review Research question:  What are capabilities, similarities, and differences in publications on NWPbased solar forecasting? (numerical weather predictions)
Method:  Research publications to the topic
 Research metrics and datasets to compare methods
 Implement and evaluate 2 methods
  Interest in Data Science
 Programming skills in a highlevel language
 Simon Zollner simon.zollner@tum.de

SOH_3D_MILP  Implementation of thermal SOHmodel in JuMP
Type of research: prototyping Research question: Method: Research code for aging model Translate it to new language JuMP Compare performance with original implementation
  Interest in Optimization
 Basic programming skills in a highlevel language
 Simon Zollner simon.zollner@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 
DATA_CURATION  The Curation Gold Standard Research Method: Literature Review & Prototyping Research Question:  What opensource data curation tools are available?
 How can these be integrated into existing software?
 How can the performance of these tools be evaluated?
 How effective are these tools?
Possible Approach:  Thorough literature review on opensource data curation tools
 Application and evaluation of the tools on opensource energy data
  Passion for data
 Structured way of working
 Annika Schneider a.k.schneider@tum.de 
Template  Template (Copy line and adapt) Research method: literature review, modelling, experimental, ... Research question: Possible approach: 


 


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