Data Processing for Utility Infrastructure

Our group aims to tackle the challenges faced in the utility/industrial/real estate infrastructure through statistical, Machine Learning and Deep Learning approaches with an emphasis to mitigate climate change. To this end, we focus on digitizing and integrating data from heterogenous sources, embedding knowledges and enable techniques that increase operational efficiency and reduce carbon emissions. We deal with time-series data (e.g., consumption data from smart meters), GIS data (e.g., network topology, geolocations of network components), SCADA data (network performance, e.g., pumps). Our current research revolves around database modelling, development of digital twin for utility networks (e.g., as a knowledge graph) as well as ML and DL algorithms for different applications such as load predictions, time-series clustering, anomaly detection/localization and data exploration.

UtilityTwin Start: September 1, 2021 End: August 31, 2024 Description:

In the UtilityTwin research project, an intelligent digital twin for any energy or water supply network is to be researched and developed on the basis of adaptive high-resolution sensor data (down to the sub-second range) and machine learning techniques. Overall, the technology concepts BigData and AI are to be combined in an innovative way in this research project in order to make positive contributions to the implementation of the energy transition and to counteract climate change.

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, , , Prediction of Household-level Heat-Consumption using PSO enhanced SVR Model. In: Tackling Climate Change with Machine Learning: workshop at NeurIPS 2021, 2021 Access via: https://www.climatechange.ai/events/neurips2021.html#accepted-works
, , , , , , , , Implications of Experiment Set-Ups for Residential Water End-Use Classification. In: Water, , 2021, DOI: 10.3390/w13020236 Access via: https://www.mdpi.com/2073-4441/13/2/236

Student Assistant / HiWi –  Knowledge Graph for Utility Data Contact person: Dr. Siming Bayer, Email: siming.bayer@fau.de You are interested in working with time series, geographical information system, different database technology and would like to develop further in the field of machine learning? Then have a look at our offer! Your tasks are:
  • Understand the state-of-the-art methods and technology for knowledge graph, identify various application fields in the industry
  • Support the development of our framework to establish a digital twin for utility infrastructure written in Python
  • Support the maintenance of graphical database for utility data deployed using Neo4j on MS AZURE environment
  • Support the enrichment and expansion of the knowledge graph by utilizing ML techniques
What you bring to the table:
  • You are currently enrolled as a student at Friedrich-Alexander University and studying computer science, mathematics, physics, or a related field
  • Hands-on programming experience with Python and familiar with machine learning techniques
  • Preferably prior experience with various database techniques, such as SQL or graphical database
  • Curiosity and pro-active mind
What you can expect from us:
  • An interesting application-oriented new field of research with contribution for sustainable utilization of nature resources
  • Extensive scientific support
  • Flexible way of working
  • Friendly and open environment at the Pattern Recognition Lab.

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