If not indicated otherwise, topics can be worked on in English or German.

If you are interested in working on one of these topics, please get in contact with the related colleague via email.
Please include a CV and academic record sheet (transcript of records) in your request.

Additional topics may be available on request. Please contact directly the scientific staff members dealing with with the field of research (see homepage) that fits your interests.

This also applies to requests for supervision of external theses or internships. Please note that we will only supervise these if the topic fits into our field of research and is of interest to us.

BT:  Bachelor's Thesis
MT: Master's Thesis
RI:  Research Internship

Wenn nicht anders angegeben, können die Themen in Deutsch oder Englisch bearbeitet werden.

Wenn Sie sich für die Bearbeitung eines der Themen interessieren, kontaktieren Sie bitte die angegebene Kotaktperson per Email. Bitte senden Sie Ihren Lebenslauf und eine Übersicht Ihrer bisherigen Studienleistungen mit.

Weitere Themen sind evtl. auf Nachfrage verfügbar. Kontaktieren Sie hier bitte direkt die wissenschafltichen Mitarbeiter*innen, die sich mit dem Themengebiet beschäftigen (siehe Homepage), das zu Ihren Interessen passt.

Dies gilt ebenso für Anfragen zur Betreuung externer Arbeiten. Bitte beachten Sie hierbei, dass diese nur von uns betreut werden, wenn das Thema in unser Arbeitsgebiet passt und für uns interessant ist.

BT:  Bachelorarbeit
MT: Masterarbeit
IP:   Ingenieurspraxis
RI:   Forschungspraxis


Type
(BT,MT,RI)
Topic
(with short description)
Contactpossible
start date

MT, BT, RI, Projektpraktikum,
Ingenieurspraxis,
Forschungspraxis,
Research Internship

Optimization under Uncertainty in the Energy Sector

Research method: Protoytping, Modelling

Research questions

  • How can we quantify the uncertainty via probabilistic forecasts of, e.g., PV production and load?
  • How can we leverage this information via optimization with extensions like e.g. stochastic optimization and Reinforcement Learning for optimal scheduling of energy storages or sizing of PV and energy storages?
  • What are suitable models for an Energy Management System (EMS), what are their tradeoffs (e.g. accuracy vs. computational effort)?

Possible approach

  • Literature Research of State of the Art
  • Find Research Gap 
  • Implement own ideas


Suggested Readings:

Concepts and Tools:

  • (Linear) Optimization: Pyomo, linopy, JuMP
  • Time Series Forecasting: sktime, nixtla
  • Machine Learning: scikit learn


Simon Zollner

simon.zollner@tum.de


winter semester 2024/25
BT, MT

Spatio-Temporal Forecasting of Wind Power

Research method: Modeling

Research question:

How can spatial information improve temporal-only forecasting methods?

Possible approach

  • Literature research on temporal and spatio-temporal methods for wind power forecasting (e.g., copula models, flow-based generators, graph neural networks, ARIMA model, RNNs, ...)

  • Definition of benchmark data sets and state-of-the-art temporal forecasting methods
  • Implementation of spatio-temporal and banchmark temporal methods of choice
  • Evaluation of its performance compared to the benchmark

Your background/interests:

  • Passion for working with data
  • Motivated to program in Python, ideally already some basic Python programming skills
  • Interest in understanding statistical/ML methods conceptually

Annika Schneider

annikakristina.schneider@tum.de 

On a rolling basis.
The topic can be defined to focus on your interests - don't hesitate to reach out.

MT, BT, RI, Projektpraktikum,
Ingenieurspraxis,
Forschungspraxis,
Research Internship

Probabilistic PV / Load Forecasts

Research method: Protoytping, Modelling

Research questions

  • How can we quantify the uncertainty via probabilistic forecasts of, e.g., PV production and load?
  • What constraints do we face on the algorithm side if we want to have embedded forecasts?
  • How can we use conformal prediction to go from point forecasts to probabilistic forecasts?

Possible approach

  • Literature Research of State of the Art of PV or Load Forecasting
  • Find Research Gap 
  • Implement own ideas
  • Validate Results e.g. with PV on institute's roof

probabilistic-forecasting-graph


Suggested Readings:

Concepts and Tools:

  • Python/Julia: pandas, DataFrames.jl
  • Time Series Forecasting and Machine Learning: sktime, functime, Darts, scikit learn, MAPIE


Simon Zollner

simon.zollner@tum.de


winter semester 2024/25

MT, RI, Projektpraktikum, Ingenieurspraxis, Forschungspraxis

Home Energy Management Systems Benchmarking Laboratory

Research method: Prototyping

Research question:

How can Home Energy Management Systems (EMS) be benchmarked in a Laboratory setup?

Possible approach:

  • Literature review on benchmarking of HEMS

  • Selection of exemplary HEMS publications, especially focusing methods like Reinforcement Learning that have public code accessible

  • Analysis of different scenarios and definition of requirements for Laboratory Setup for benchmarking HEMS

  • Implementation of the benchmarking setup in the laboratory using load emulators

  • Execution and Analysis of benchmarking process for exemplary HEMS publication methods


Sebastian Eichhorn
sebastian.eichhorn@tum.de

anytime
MT, RI, Projektpraktikum, Forschungspraxis

Real World Distribution Grid Topology Clustering

Research method: Prototyping

Research question:

How can real world low voltage distribution grids be clustered and and categorized? What is a collection of parameters describing a wide range of real world distribution grids?

Possible approach:

  • Literature review on LV distribution grids, allocation methods and existing benchmarking networks

  • Collection of existing LV distribution grids, generation from open accessible data (e.g. OpenStreetMaps) using ML tools for data aggregation

  • Clustering and Analysis

  • Derivation of parameter collection


Sebastian Eichhorn
sebastian.eichhorn@tum.de

anytime
MT, RI, Projektpraktikum, Forschungspraxis

EnergyOS: Self-discovery of Apps and Resources within a distributed Multi-App EMS Platform

Research method: Prototyping

Research question:

How can apps find, address, and identify other apps in EnergyOS?
How do they know which data, resources and hardware is available to them?

Possible approach:

  • Creation and comparison of different discovery and naming schemes

    • eg. (1) apps learn about each other through a separate channel outside of EnergyOS (for example: DSO announcing their app in a contract with a building), (2) public app addresses and list of resources upon connection, (3) like 2 but with restricted app and resource groups enforced by EnergyOS, (4) etc.

  • Evaluation of schemes, their benefits and what a typical procedure would look like starting from app installation.

  • Implementation of discovery scheme in EnergyOS, with relevant demo.

Your background/interests:

  • Energy Management Systems
  • Distributed Systems
  • Programming experience helpful (python)

Background:

Current Energy Management Systems are monolithic systems with only one goal in mind and only a limited view of their environments. However, they often operate in parallel to other EMS systems that access the same resources, contain useful and relevant information for them, and/or have conflicting system objectives. EnergyOS is a connected EMS platform that allows multiple EMS apps to communicate, plan, and resolve these types of conflicts natively, by providing functionality for distributed resource management and control of EMS components. 

Ehimare Okoyomon
e.okoyomon@tum.de

flexible – please contact me!
MT, RI, Projektpraktikum, Forschungspraxis

EnergyOS: Operating System Internal Resource and Data Management

Research method: Literature Review, Prototyping

Research question:

How can we store and retrieve data most efficiently?
Can we integrate caching and parallelization of requests?

Possible approach:

  • Literature review on data access and storage for time series applications, and data caching.

  • Creation and comparison of data management schemes
    • e.g. resource data stored using: (1) python data structures (local dictionaries and lists), (2) json files, (3) csv files, (4) common database like Postgres or MongoDB, (5) specialized time series database like InfluxDB.
  • Evaluation of schemes, their performance, how they scale (i.e. complexity in terms of time and storage).

  • Design of data caching procedure and parallelization of OS tasks.
  • Implementation of Resource and Data Management design in EnergyOS.

Your background/interests:

  • Energy Management Systems
  • Data Management / Databases
  • Programming experience helpful (python)

Background:

Current Energy Management Systems are monolithic systems with only one goal in mind and only a limited view of their environments. However, they often operate in parallel to other EMS systems that access the same resources, contain useful and relevant information for them, and/or have conflicting system objectives. EnergyOS is a connected EMS platform that allows multiple EMS apps to communicate, plan, and resolve these types of conflicts natively, by providing functionality for distributed resource management and control of EMS components. 

Ehimare Okoyomon
e.okoyomon@tum.de

flexible – please contact me!
MT, RI, Projektpraktikum, Forschungspraxis

EnergyOS: Scheduling and OS-controlled Schedule Generation in a Multi-App EMS Platform

Research method: Literature Review, Prototyping

Research question:

What level of control should the EnergyOS have in creating and executing operation plan schedules for energy components?
How can this be used in practice and what are the advantages and challenges of such an approach?

Background:

Current Energy Management Systems are monolithic systems with only one goal in mind and only a limited view of their environments. However, they often operate in parallel to other EMS systems that access the same resources, contain useful and relevant information for them, and/or have conflicting system objectives. EnergyOS is a connected EMS platform that allows multiple EMS apps to communicate, plan, and resolve these types of conflicts natively, by providing functionality for distributed resource management and control of EMS components.

Possible approach:

  • Literature review of common EMS scheduling / optimization problems and state of the art methods for solving.

  • Implementation of one or more scheduling solutions in EnergyOS.
  • Creation of an interface to provide EnergyOS with a set of constraints, objectives, and data/resources.
  • Selection and implementation of use cases (EnergyOS apps) to demonstrate how OS-controlled scheduling works.

Your background/interests:

  • Energy Management Systems
  • Optimizations
  • Programming experience helpful (python)

Ehimare Okoyomon
e.okoyomon@tum.de

flexible – please contact me!
BT, MT, RI, Forschungspraxis

Project EDL: Building a Digital Platform for Energy Data and ML Models - Web Development

Research method: Prototyping

Research question:

How can we implement the web interface of a tool to serve energy researchers with data and code for a specific use case?

Possible approach:

  • Research on platform concepts in other research fields (e.g., How did Hugging Face evolve?)
  • Implementation of required components of the platform (backend and/or frontend)
  • Testing and evaluation of its performance

Your background/interests:

  • Passion for programming
  • Interest in web-development, ideally some background in one or more of the following: Python, JavaScript, TypeScript, PostgreSQL
  • Curiosity about how energy research infrastructure can be set up efficiently

For more info see here!

flexible
BT, MT, RI, Forschungspraxis

Project EDL: Building a Digital Platform for Energy Data and ML Models - Data Curation

Research method: Prototyping

Research question:

How can we curate energy data such that researchers can quickly understand and use them? How can we automate this process?

Possible approach:

  • Research for open-source, high-quality energy data sets
  • Implementing of Jupyter Notebooks to document and visualize the data
  • Evaluation of the helpfulness of the curation process
  • Developing of ideas of automating this process

Your background/interests:

  • Passion for data
  • Interest in data analysis, ideally some background in Python
  • Curiosity about how energy research can be accelerated

For more info see here!

Annika Schneider

a.k.schneider@tum.de 

flexible
BT, MT, RI, Forschungspraxis

Project EDL: Building a Digital Platform for Energy Data and ML Models - Code & Tool Curation

Research method: Prototyping

Research question:

How can we curate energy model code and open-source tools such that researchers can quickly understand and use them? How can we automate this process?

Possible approach:

  • Research for open-source, high-quality energy model implementations and tools
  • Implementing of Jupyter Notebooks to document the usage of the code
  • Evaluation of the helpfulness of the curation process
  • Developing of ideas of automating this process

Your background/interests:

  • Passion for playing around with code and tools
  • Interest in energy system models, ideally some background in Python
  • Curiosity about how energy research can be accelerated

For more info see here!

Annika Schneider

a.k.schneider@tum.de 

flexible
BT, MT, RI, Forschungspraxis

Project EDL: Building a Digital Platform for Energy Data and ML Models - Database Infrastructure

Research method: Prototyping

Research question:

How can we store diverse energy data sets in an efficient and secure database infrastructure?

Possible approach:

  • Developing of pipelines importing data in a database in a sensible, accesible format
  • Implementing of Jupyter Notebooks to showcase how data can be requested from the database
  • Evaluation of the effiency and security of this process

Your background/interests:

  • Passion for data
  • Interest in databases, ideally some background in databases (e.g. MongoDB)
  • Curiosity about how energy research can be accelerated

For more info see here!

Annika Schneider

a.k.schneider@tum.de 

flexible
BT, MT, RI, Forschungspraxis

Development of Smart Grid Simulator (Project EDGE)

Research method: Prototyping

Research questions:

  • How can models of energy system components be containerized and deployed on a compute cluster?
  • How can performance, scalability, and reliability of this simulation infrastructure be measured?
  • How can basic EMS scenarios be simulated and analyzed using this infrastructure? 

Possible approach:

  • Setup local container development and cluster environment (Docker, Kubernetes, etc.)

  • Develop several energy system components as Docker containers exposing web-based interfaces (e.g., distribution grid, HEMS, HH energy demand simulator, solar PV simulator, battery charge controller, etc.)
  • Implement message-based communication between containers running on cluster
  • Measure performance in different scenarios  

Resources:

Christoph Goebel

christoph.goebel@tum.de

Sebastian Eichhorn

sebastian.eichhorn@tum.de

Elgin Kollnig 

elgin.kollnig@tum.de

flexible – please contact us!
BT, MT, RI, Forschungspraxis

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 code applying deep learning to estimate distribution grid states
  • Benchmark with state of the art methods

Christoph Goebel

christoph.goebel@tum.de

flexible – please contact me!
BT, MT, RI, Forschungspraxis

Project EDGE: Benchmarking of Monolithic Simulation Tools against Cluster Based Simulations

Research method: Prototyping

Research question:

  • In which scenarios is a cluster based simulation faster than monolithic simulation tools?
  • How does it differ between time series simulations and single state simulations?
  • How can those differences be quantified?

Possible approach

  • Understand EDGE cluster concept and other monolithic tools
  • Define Scenarios and Parameters for which to investigate
  • Benchmark with state of the art methods

Sebastian Eichhorn

sebastian.eichhorn@tum.de


anytime
BT, MT, RI, Forschungspraxis

Project EDGE: Investigation of Scalable Methods for Load Flow Calculations

Research method: Prototyping

Research question:

  • How can large power grids (distribution grids, optionally connected by transmission grid) be split up in smaller part grids without losing precision in load flow and transient simulations?
  • For which scenarios does this make sense, trading off the increased parallelization of calculation with increased overhead
  • How can those differences be quantified?

Possible approach

  • Understand EDGE cluster concept and other monolithic approaches
  • Define example power grids and investigate strategies of power grid calculation parallelization
  • Setup prototype for example power grid, ensuring precision while clustering the simulation
  • Benchmark with state of the art methods

Sebastian Eichhorn

sebastian.eichhorn@tum.de


anytime

Supervisors see also → Processing for Theses (Bachelor/Master)

Betreuer siehe auch → Abwicklung von Abschlussarbeiten

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