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.


AI-refined thermo-hydraulic model for the improvement of the efficiency and quality of water supply

Start: November 1, 2023

End: October 31, 2026


The United Nations' goals for sustainable development have made improving quality of life and access to clean drinking water a political priority. However, in recent decades, the water cycle in Bavaria has also been significantly affected by climate change. Two important aspects of daily drinking water supply and distribution are the assurance of water quality and the increase in usage efficiency. To enhance the resilience and capacity of the water supply in general, numerical simulation, data integration, and artificial intelligence (AI) are necessary. In this project, we aim to develop an AI-refined temperature-hydraulic model using heterogeneous data sources from a Bavarian water supply network. Hybrid AI methods are employed to model the complex relationship between water and soil temperature. The resulting model will serve as the basis for various real applications such as leak detection, anomaly recognition, and monitoring of drinking water quality, with the overarching goal of increasing the efficiency and quality of the water supply while simultaneously contributing to the containment of the impact of climate change on drinking water supply


Start: September 1, 2021

End: August 31, 2024


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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, , , , , Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble. In: NeurIPS 2022 Workshop Tackling Climate Change with Machine Learning, 2022

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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

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, , , , , , , , Implications of Experiment Set-Ups for Residential Water End-Use Classification. In: Water, , 2021, DOI: 10.3390/w13020236

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