Adithya Ramachandran, M. Sc.

Chair of Computer Science 5 (Pattern Recognition)
Research associates

Address

Martensstraße 3
91058 Erlangen

10.138, 10

Contact

adithya.ramachandran@fau.de
lme.tf.fau.de/our-team/adithya-ramachandran/

Adithya Ramachandran UtilityTwin

My research focuses on modeling a digital twin and developing methods for water and district heating 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 and state-of-the-art ML and DL techniques, this research intends to place itself as a framework that facilitates efficient water and district heating networks.

 

  • Utility Data Processing: GIS data, Smart meter data, SCADA data, Cadastre maps
  • Machine Learning & Deep Learning: Forecasting, Anomaly detection, Event detection, Network optimization, Demand clustering, Document digitization, Graph database, LLM agents

 

 

No topics available.

  • Utility Data Processing: GIS data, Smart meter data, SCADA data, Cadastre maps
  • Machine Learning & Deep Learning: Forecasting, Anomaly detection, Event detection, Network optimization, Demand clustering, Document digitization, Graph database, LLM agents

2021

  • UtilityTwin

    (Third Party Funds Group – Overall project)

    Project leader: , ,
    Term: September 1, 2021 - August 31, 2024
    Acronym: UtilityTwin
    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.

National Student Prize for Social Innovations (StiPS)

— Recognition for innovative research contributions for climate protection and sustainability by Bundesministerium für Forschung, Technologie und Raumfahrt.

2026

Journal Articles

2025

Conference Contributions

2024

Journal Articles

Conference Contributions

2022

Conference Contributions

Type Title Status
MA thesis Link Prediction on Utility Networks Using Graph Neural Networks finished
MA thesis Deep Learning for Geo-Referencing Historical Utility Documents With Geographical Features finished
MA thesis Real-time Path Loss Prediction Using Deep Learning for Smart Meter Communication System finished
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 finished
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