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

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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.

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


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

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 2024/2501.06.2024

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 2024/2501.06.2024

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

anytime05/2024
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

Starting in WS 2024

03/2024

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

Starting in WS 202403/2024
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!09/2023
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!09/2023

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

Betreuer siehe auch → Abwicklung von Abschlussarbeiten

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