2 citations · 2 across the 4 of their papers we have counts for
4 papers
A Deep Learning Framework for Heat Demand Forecasting using Time-Frequency Representations of Decomposed Features
Adithya Ramachandran, Satyaki Chatterjee, Thorkil Flensmark B. Neergaard +3
District Heating Systems are essential infrastructure for delivering heat to consumers across a geographic region sustainably, yet efficient management relies on optimizing diverse…
Water Demand Forecasting of District Metered Areas through Learned Consumer Representations
Adithya Ramachandran, Thorkil Flensmark B. Neergaard, Tomás Arias-Vergara +2
Advancements in smart metering technologies have significantly improved the ability to monitor and manage water utilities. In the context of increasing uncertainty due to climate c…
Advancing Heat Demand Forecasting with Attention Mechanisms: Opportunities and Challenges
Adithya Ramachandran, Thorkil Flensmark B. Neergaard, Andreas Maier +1
Global leaders and policymakers are unified in their unequivocal commitment to decarbonization efforts in support of Net-Zero agreements. District Heating Systems (DHS), while cont…
Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble
Adithya Ramachandran, Satyaki Chatterjee, Siming Bayer +2
One of the primal challenges faced by utility companies is ensuring efficient supply with minimal greenhouse gas emissions. The advent of smart meters and smart grids provide an un…