Search Results for author: Ahmed S. Zamzam

Found 11 papers, 4 papers with code

Unsupervised Learning for Equitable DER Control

no code implementations17 Mar 2024 Zhenyi Yuan, Guido Cavraro, Ahmed S. Zamzam, Jorge Cortés

In the context of managing distributed energy resources (DERs) within distribution networks (DNs), this work focuses on the task of developing local controllers.


Non-Stationary Policy Learning for Multi-Timescale Multi-Agent Reinforcement Learning

no code implementations17 Jul 2023 Patrick Emami, Xiangyu Zhang, David Biagioni, Ahmed S. Zamzam

In detail, we theoretically demonstrate that the effects of non-stationarity introduced by multiple timescales can be learned by a periodic multi-agent policy.

energy management Inductive Bias +3

Interpreting Primal-Dual Algorithms for Constrained Multiagent Reinforcement Learning

1 code implementation29 Nov 2022 Daniel Tabas, Ahmed S. Zamzam, Baosen Zhang

Constrained multiagent reinforcement learning (C-MARL) is gaining importance as MARL algorithms find new applications in real-world systems ranging from energy systems to drone swarms.


PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems

1 code implementation10 Nov 2021 David Biagioni, Xiangyu Zhang, Dylan Wald, Deepthi Vaidhynathan, Rohit Chintala, Jennifer King, Ahmed S. Zamzam

We present the PowerGridworld software package to provide users with a lightweight, modular, and customizable framework for creating power-systems-focused, multi-agent Gym environments that readily integrate with existing training frameworks for reinforcement learning (RL).

Multi-agent Reinforcement Learning reinforcement-learning +1

OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets

1 code implementation1 Nov 2021 Trager Joswig-Jones, Kyri Baker, Ahmed S. Zamzam

Increasing levels of renewable generation motivate a growing interest in data-driven approaches for AC optimal power flow (AC OPF) to manage uncertainty; however, a lack of disciplined dataset creation and benchmarking prohibits useful comparison among approaches in the literature.


Enhancement of Distribution System State Estimation Using Pruned Physics-Aware Neural Networks

no code implementations7 Feb 2021 Minh-Quan Tran, Ahmed S. Zamzam, Phuong H. Nguyen

Realizing complete observability in the three-phase distribution system remains a challenge that hinders the implementation of classic state estimation algorithms.

PHASED: Phase-Aware Submodularity-Based Energy Disaggregation

no code implementations1 Oct 2020 Faisal M. Almutairi, Aritra Konar, Ahmed S. Zamzam, Nicholas D. Sidiropoulos

Energy disaggregation is the task of discerning the energy consumption of individual appliances from aggregated measurements, which holds promise for understanding and reducing energy usage.

Model-Free State Estimation Using Low-Rank Canonical Polyadic Decomposition

no code implementations13 Apr 2020 Ahmed S. Zamzam, Yajing Liu, Andrey Bernstein

As electric grids experience high penetration levels of renewable generation, fundamental changes are required to address real-time situational awareness.


GRATE: Granular Recovery of Aggregated Tensor Data by Example

no code implementations27 Mar 2020 Ahmed S. Zamzam, Bo Yang, Nicholas D. Sidiropoulos

In this paper, we address the challenge of recovering an accurate breakdown of aggregated tensor data using disaggregation examples.

Energy Storage Management via Deep Q-Networks

no code implementations26 Mar 2019 Ahmed S. Zamzam, Bo Yang, Nicholas D. Sidiropoulos

Energy storage devices represent environmentally friendly candidates to cope with volatile renewable energy generation.

Management Reinforcement Learning (RL)

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