Search Results for author: Manu Lahariya

Found 7 papers, 1 papers with code

Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households

no code implementations29 Oct 2023 Gargya Gokhale, Niels Tiben, Marie-Sophie Verwee, Manu Lahariya, Bert Claessens, Chris Develder

Given its substantial contribution of 40\% to global power consumption, the built environment has received increasing attention to serve as a source of flexibility to assist the modern power grid.

energy management Reinforcement Learning (RL)

ML framework for global river flood predictions based on the Caravan dataset

no code implementations14 Nov 2022 Ioanna Bouri, Manu Lahariya, Omer Nivron, Enrique Portales Julia, Dietmar Backes, Piotr Bilinski, Guy Schumann

Here, we offer the first global river flood prediction framework based on the newly published Caravan dataset.

Physics Informed LSTM Network for Flexibility Identification in Evaporative Cooling Systems

no code implementations19 May 2022 Manu Lahariya, Farzaneh Karami, Chris Develder, Guillaume Crevecoeur

These physics informed networks approximate the time-dependent relationship between control input and system response while enforcing the dynamics of the process in the neural network architecture.

BIG-bench Machine Learning

Optimized cost function for demand response coordination of multiple EV charging stations using reinforcement learning

no code implementations3 Mar 2022 Manu Lahariya, Nasrin Sadeghianpourhamami, Chris Develder

However, we note that the computationally expensive cost function adopted in the previous research leads to large training times, which limits the feasibility and practicality of the approach.

Reinforcement Learning (RL)

Defining a synthetic data generator for realistic electric vehicle charging sessions

1 code implementation28 Feb 2022 Manu Lahariya, Dries Benoit, Chris Develder

Addressing this need for publicly available and realistic data, we develop a synthetic data generator (SDG) for EV charging sessions.

Learning physics-informed simulation models for soft robotic manipulation: A case study with dielectric elastomer actuators

no code implementations25 Feb 2022 Manu Lahariya, Craig Innes, Chris Develder, Subramanian Ramamoorthy

We simulate the task of using DEA to pull a coin along a surface with frictional contact, using FEM, and evaluate the physics-informed model for simulation, control, and inference.

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