UtilityTwin

(Third Party Funds Group – Overall project)

Overall project:
Project leader: Andreas Maier, Siming Bayer
Project members:
Start date: September 1, 2021
End date: August 31, 2024
Acronym: UtilityTwin
Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
URL:

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

Chatterjee S., Bayer S., Maier A.:
Prediction of Household-level Heat-Consumption using PSO enhanced SVR Model
Tackling Climate Change with Machine Learning: workshop at NeurIPS 2021 (Online, December 14, 2021 - December 14, 2021)
In: Tackling Climate Change with Machine Learning: workshop at NeurIPS 2021, https://www.climatechange.ai/events/neurips2021.html#accepted-works: 2021
Open Access: https://www.climatechange.ai/papers/neurips2021/42/paper.pdf
URL: https://www.climatechange.ai/events/neurips2021.html#accepted-works
BibTeX: Download
Chatterjee S., Ramachandran A., Neergaard TF., Maier A., Bayer S.:
Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble
NeurIPS 2022 Workshop Tackling Climate Change with Machine Learning (Hybrid, December 9, 2022 - December 9, 2022)
In: NeurIPS 2022 Workshop: Tackling Climate Change with Machine Learning 2022
Open Access: https://www.climatechange.ai/papers/neurips2022/46
URL: https://www.climatechange.ai/papers/neurips2022/46
BibTeX: Download