Description

  • Participants of the course are allocated individual research topics to work over the seminar course. Group works are possible.
  • A kickoff session will be conducted to introduce the course and topics. Students can already apply before the kickoff session, or shortly after.
  • Regular follow-up meetings with the participants and supervisor are held to aid the participants with the seminar tasks.

The seminar tries to mimic the event flow of a conference publication. Therefore, the deliverables are the following:

  • a scientific paper (submitted as the seminar report).
  • a conference-style final presentation.

More details on ECTS, learning outcomes, etc is offered at the kickoff session and later on moodle.

Process and Time Line (Sommer semester 2024)

(Prospected) Registration Period: 01.03.24 - 19.04.24

Kickoff Meeting: 17.04.24, 3pm-4:30pm, room N3815.

Due date to choose top 3 topics: 19.04.24, 12 noon.

Topic allocation: 22.04.24, 7pm the latest.

Proposal Presentations: 08. & 15. of May, 2024, 3pm-4:30pm, N3815.

Final Presentations: 10. & 17. of July, 2024, 3pm-4:30pm, N3815.

Available Topics

If you are interested in working on a topic please feel free to contact the related colleague. 

AcronymTopic descriptionRequired skillsContact
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 best-performing (1) temporal and (2) spatio-temporal 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

Flow-Based Generators in the energy context

Research method: Literature Review

Research question:

  • For which tasks in the energy context can flow-based generators be applied?
  • How can the flow-based generator architecture be exploited/adopted for these specific tasks?

Possible approach

  • Learn about flow-based generators and identify their strengths, weaknesses and typical applications
  • Find existing studies of flow-based 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 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

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 state-of-the-art 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 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

 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

  • Read and review papers on different methods

  • Provide summaries in the form of tables and figures

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

  • What cyber attacks on EV charging stations are conceivable?
  • How can the impacts of theses attacks be modeled in Python?

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 skillsSebastian 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 skillsSebastian 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 skillsSebastian 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

  • Implement selected lemlab logic in Docker containers

  • Design and implement message-based communication between containers
  • Deploy containers on Kubernetes cluster (can be run locally)  

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

  • Review different tools for household energy simulation
  • Implement selected tools as Docker containers with standardized interfaces

  • Write code for comparing accuracy and computational performance 
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

  • Read and review papers on different methods

  • Provide summaries in the form of tables and figures

  • Optional: Replicate or simulate an attack

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 time-of-use tariffs?

  • Can we demo this in a real world situation? (ex. self-consumption 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:

  • How does Reinforcement Learning work with probabilistic forecasts in a Household Setting (battery storage, heat pump, thermal storage) as an EMS

Method:

  • Find in literature models already used with Reinforcement Learning with forecasts


  • Add probabilistic forecasts to the model


  • Evaluate results

Literature:

 

  • Knowledge in high-level 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 NWP-based Solar Forecasting

Type of research: literature review

Research question:

  • What are capabilities, similarities, and differences in publications on NWP-based 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 high-level language 

Simon Zollner
simon.zollner@tum.de


SOH_3D_MILP

Implementation of thermal SOH-model in JuMP

Type of research: prototyping

Research question:

  • How can we speed up SOH-computation using state-of-the-art tools?

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 high-level 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 open-source 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 open-source data curation tools
  • Application and evaluation of the tools on open-source 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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