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 
ES_EVAL  TechnoEconomic 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 stateoftheart 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 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 
PV_AGING  AgeConscious 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 PowerHardwareintheLoop (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  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 
EMS_COMP  Comparison of OpenSource Energy Management System (EMS) Research method: Literature Research Research question:  What are the (new) opensource 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 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 
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 
LDM  TUMEMT 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 NextGeneration 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 
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