Adithya Ramachandran

Adithya Ramachandran, M. Sc.

Researcher

Department of Computer Science
Chair of Computer Science 5 (Pattern Recognition)

Room: Room 10.138
Martensstr. 3
91058 Erlangen

Office hours

Each week Fr, 09:00 - 17:00, Room 10.138,

My research focuses on modeling and developing methods for water and heat utility networks to enable ML, and DL solutions that aid in decarbonization. We take a synergistic approach to model consumer, and network behavior through the underlying time-series data and Geographic Information System (GIS) data. With an end-to-end data pipeline that incorporates techniques such as outlier detection, event detection, forecasting, network optimization, etc, this research intends to place itself as a framework that facilitates efficient utility networks.

Academic CV

  • Since 10/2021:
    Ph.D. Student at the Pattern Recognition Lab, Diehl Metering GmbH
  • 10/2017 – 9/2021:
    M.Sc. in Computational Engineering
    Friedrich-Alexander University Erlangen-Nürnberg

Projects

  • UtilityTwin

Term: September 1, 2021 – 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 based on adaptive high-resolution sensor data (down to the sub-second range), GIS data, and machine learning techniques. Overall, the technology concepts BigData and AI are to be combined in an innovative way in this research project to make positive contributions to the implementation of the energy transition and to counteract climate change.

2021

  • UtilityTwin

    (Third Party Funds Group – Overall project)

    Term: September 1, 2021 - August 31, 2024
    Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
    Abstract

    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.

Publications

2024

Conference Contributions

2022

Conference Contributions

Thesis

Type Title Status
MA thesis Deep Learning for Geo-Referencing Historical Utility Documents With Geographical Features running
MA thesis Real-time Path Loss Prediction Using Deep Learning for Smart Meter Communication System running
Project TSI Challenge Summer 2024: Heat & Water Demand Forecasting finished
MA thesis Explainable Predictive Maintenance: Forecasting and Anomaly Detection of Diagnostic Trouble Codes for Truck Fleet Management running
MA thesis Leakage Detection and Modeling in Water Distribution Systems finished
MA thesis Adaptive Training for Heat Demand Prediction of District Heating Network finished
MA thesis Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble finished
MA thesis A hybrid approach forLeakage Localization in the Water Distribution Network finished
MA thesis Adaptive Training of Heat Demand Prediction using Continual Learning finished
Project Represent senor data of district heating network using contrastive learning finished
MA thesis Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble finished