Seminar Energy Infomatics

Description

Participants of the course are allocated individual research topics to work over the seminar course. Groups of two are possible.
A kickoff session will be conducted to introduce the course and topics.
Attendance is only mandatory on the proposal and final presentation dates.
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.

Topic Allocation

To apply for topics, the top 3 topics (please provide the acronyms) and the current transcript of records must be sent to a.k.schneider@tum.de. Applications will only be accepted from 25.03.25. Information on deadlines can be found in the next section. Note that you can also apply for a topic as a group of two students!

Process and Time Line (Winter semester 2024/25)

Topics are chosen in two blocks. Students who want to be sure of getting a fixed place in the seminar before the start of the semester must apply by 09.04. (12 noon). These students will be informed on 14.04. if they get a place in the seminar and which topic they will be working on.

Students who want to apply later can do so until 24.04. (7pm) and will then be informed on 28.04. whether they could still get a place in the seminar.

The most important dates for the seminar are summarized below:

Registration period: tbd

First deadline for submitting the top 3 topic requests: 09.04., 12 noon.

Assignment of topics block 1: 14.04.

Kick-off (introduction to the seminar, presentation of the topics still available): 23.04., time and room tbd

2. deadline for submission of the top 3 topic requests: 24.04. (19 Uhr).

Assignment of topics block 2: 28.04.

Proposal Presentations: 14 & 21 Mai, time and room tbd

Final Presentations: 16. & 23. Juli, time and room tbd

THE APPLICATION FOR WINTER SEMESTER 24/25 IS CLOSED.

Acronym	Topic description	Required skills	Group work possible (group of two)	Contact	Time Topic Added
GRID_GEN	Distribution Grid Model Generation Methods Research method: Prototyping Research question: How can realistic distribution grid models be synthesized? How can synthesized distribution grid models be evaluated? Possible approach: Understand state-of-the-art based on literature Develop efficient Python or Julia code to implement distribution grid generation method Evaluate method using actual data	Python/ Julia programming skills Understanding of distribution grid modelling		Christoph Goebel christoph.goebel@tum.de	03/2025
SOLAR_FC	Probabilistic Solar PV Power Forecasting Research method: Prototyping Research question: How can solar PV power output be forecasted probabilistically based on weather forecasts? How can corresponding methods be implemented and benchmarked? Possible approach: Understand state-of-the-art based on literature Develop efficient Python or Julia code to implement probabilistic forecasting method Evaluate method using actual data	Python/ Julia programming skills		Christoph Goebel christoph.goebel@tum.de	03/2025
WPF_IMAPCT	Economic Impact of Renewable Power Forecasting Error Research method: Prototyping Research question: How can the economic impact of renewable power forecasting errors be assessed? How much money can a wind power plant save by improving its forecast? Possible approach: Understand state-of-the-art based on literature Develop efficient Python or Julia code to implement assessment method Evaluate method using actual data	Python/ Julia programming skills Understanding of electricity markets		Christoph Goebel christoph.goebel@tum.de	03/2025
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	09/2023
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	09/2023
ES_EVAL	Techno-Economic 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 state-of-the-art 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	04/2024
P2P_ENERGY_TRADING	Decentralized P2P Energy Trading Under Network Constraints Research method: Prototyping Research question: How can peer to peer energy trading in distribution grids be realized while respecting physical constraints? Which approaches exist? How can these approaches be benchmarked using realistic distribution grid models? Possible approach: Understand state-of-the-art based on literature Develop efficient Python or Julia code implementing existing methods Evaluate performance of methods using realistic models of distribution grids	Python/ Julia programming skills		Christoph Goebel christoph.goebel@tum.de	09/2024
MULTI_PERIOD_OPF	Computing Multi-Period Optimal Power Flow in Distribution Grids Research method: Prototyping Research question: Which methods can be used to calculate multi-period optimal power flow in distribution grids? How do these methods scale for different distribution grid sizes? Possible approach: Understand state-of-the-art models based on literature Formulate mathematical optimization problem Develop efficient Python or Julia code using state-of-the-art methods to solve the problem Evaluate solution method using realistic distribution grids with solar, load, and storage	Python/ Julia programming skills		Christoph Goebel christoph.goebel@tum.de	09/2024
PINN_LOADFLOW	Benchmarking Physics-Informed Neural Networks for Distribution Grid Load Flow Calculation Research method: Prototyping Research question: How can the method of physics-informed NNs be used to calculate load flows in power distribution systems? How do these methods perform compared to standard numerical optimization methods? Possible approach: Understand state-of-the-art models based on literature Develop efficient Python or Julia code implementing one of the methods Compare PINN-based approach to numerical optimization based on quality and solving speed	Python/ Julia programming skills First experience with deep learning		Christoph Goebel christoph.goebel@tum.de	09/2024
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/ Julia programming skills First experience with deep learning		Christoph Goebel christoph.goebel@tum.de	09/2023
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/ Julia programming skills		Christoph Goebel christoph.goebel@tum.de	09/2023
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/ Julia programming skills		Christoph Goebel christoph.goebel@tum.de	09/2023
PINN_STATE_ESTIMATION	Benchmarking Physics-Informed Neural Networks for Distribution Grid State Estimation Research method: Prototyping Research question: How can the method of physics-informed NNs be used to predict the state of power distribution systems? How do these methods perform compared to standard numerical optimization methods? Possible approach: Understand state-of-the-art models based on literature Develop efficient Python or Julia code implementing one of the methods Compare PINN-based approach to numerical optimization based on quality and solving speed	Python/ Julia programming skills First experience with deep learning		Christoph Goebel christoph.goebel@tum.de	09/2024
WEATHER_FC	Generating Weather Forecasts with Open-Source Tools Research method: Prototyping Historical weather forecasts, i.e., weather forecasts from past years, are required in many scientific projects but oftentimes not available for free. Recently published open-source weather forecast tools might be a good solution to generate such historical forecasts yourself. Research question: How can we use open-source tools like Google's NeuralGCM or Microsoft's Aurora to generate historical weather forecasts? How closely do these generated forecasts match real historical forecast data? Possible approach: Learn to use NeuralGCM and Aurora. Generate forecasts for weather variables like temperature, wind speed, etc. Evaluate the forecasts by comparing the forecasts to real historical forecast data.	Python programming skills (or high motivation to learn Python)	yes	Annika Schneider a.k.schneider@tum.de	02/2025
COMP_ENERGY	Evaluating the Energy Impact of Computation for Machine Learning Research method: Literature Review / Prototyping Research question: How is energy estimation of computation done in research and how do the methods compare? For ML applications, does faster training always mean more energy efficient, or are there other model or pipeline design considerations? What are the popular libraries for measuring energy impact of computation for python and what do they provide? (Stretch) Using one of these libraries, can we benchmark and compare the energy impact of one or more ML models in the EMT context (ex. solar or wind power prediction)? 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	04/2024
GRID_TARIFF	Comparison of the influence of different variable grid tariffs Research method: Literature review Research question: What options are there for designing variable grid tariffs? What effects do the different variable grid tariffs have on grid utilization and profitability for end consumers? Possible approach: Identification of different variable grid tariffs Identification of the advantages and disadvantages of the different grid tariffs (Development of new options for variable grid tariffs)			Elgin Kollnig elgin.kollnig@tum.de	09/2024
FED_HOU	Federated machine learning for household load forecasting Research method: Prototyping Research question: Can we forecast household load without seeing a household's sensitive data? Which forecasting models are suitable for a federated setting? Possible approach: Literature review on federated forecasting Find & preprocess suitable datasets Build federated forecasting pipeline Compare results vs. non-federated forecasting methods	Programming skills in Python Basic statistics/ML knowledge		Jan Marco Ruiz de Vargas janmarco.ruiz@tum.de	04/2024
COR_ENCR	Fast computation of correlation measures with homomorphic encryption Research method: modeling Research question: Can two parties owning a data vector compute a correlation measure together, without revealing their private data to each other? Can we devise a (partial/ fully) homomorphic encryption scheme to calculate various correlation measures? How fast can this scheme be? Possible approach: Understand homomorphic encryption Understand correlation measures Model communication & computation protocol			Jan Marco Ruiz de Vargas janmarco.ruiz@tum.de	09/2024
SINDY_BAT	SINDy (Sparse Identification of Nonlinear Dynamical Systems)-based Battery Aging Model Research method: Prototyping Research question: How to implement SINDy to formulate battery aging problem? How to design the a feature library theta for SINDy model, and how different could the results be? How does the SINDy model perform comparing to other battery aging model (e.g. NNs / other symbolic regression model etc..)? Possible approach: Literature review on SINDy and battery aging prediction Implement SINDy model and train with existing dataset Compare the results with the existing battery aging model.	Programming skills in Python Basic ML and/or Battery aging knowledge		Sheng Yin sheng.yin@tum.de	09/2024
MARL_EMS	Multi Agent Reinforcement Learning (MARL) for Power Split Control Research method: Prototyping Research question: How can MARL solve power split control problem? Can MARL out-perform centralized RL? Possible approach: Literature review on MARL and RL in power split control Build RL and MARL environment for Power split Control Implement Basic RL and MARL algorithm to compare the results	Programming skills in Python Basic ML and/or RL knowledge		Sheng Yin sheng.yin@tum.de	09/2024

Seitenhierarchie

Seminar Energy Infomatics

Topic Allocation

Process and Time Line (Winter semester 2024/25)

THE APPLICATION FOR WINTER SEMESTER 24/25 IS CLOSED.

Decentralized P2P Energy Trading Under Network Constraints

Computing Multi-Period Optimal Power Flow in Distribution Grids

Benchmarking Physics-Informed Neural Networks for Distribution Grid Load Flow Calculation

Benchmarking Physics-Informed Neural Networks for Distribution Grid State Estimation

Generating Weather Forecasts with Open-Source Tools