Search Results for author: Yize Chen

Found 16 papers, 9 papers with code

Targeted Demand Response: Formulation, LMP Implications, and Fast Algorithms

no code implementations27 Nov 2022 Yufan Zhang, Honglin Wen, Tao Feng, Yize Chen

Numerical studies demonstrate compared with the benchmarking strategy, the proposed approach can reduce the price to the reference point with less efforts in demand reduction.

BEAR: Physics-Principled Building Environment for Control and Reinforcement Learning

1 code implementation27 Nov 2022 Chi Zhang, Yuanyuan Shi, Yize Chen

Recent advancements in reinforcement learning algorithms have opened doors for researchers to operate and optimize building energy management systems autonomously.

energy management Management +1

Carbon-Aware EV Charging

1 code implementation26 Sep 2022 Kai-Wen Cheng, Yuexin Bian, Yuanyuan Shi, Yize Chen

This paper examines the problem of optimizing the charging pattern of electric vehicles (EV) by taking real-time electricity grid carbon intensity into consideration.

Total Energy

Learning Task-Aware Energy Disaggregation: a Federated Approach

1 code implementation14 Apr 2022 Ruohong Liu, Yize Chen

We consider the problem of learning the energy disaggregation signals for residential load data.

Federated Learning Meta-Learning +1

Adam-based Augmented Random Search for Control Policies for Distributed Energy Resource Cyber Attack Mitigation

no code implementations27 Jan 2022 Daniel Arnold, Sy-Toan Ngo, Ciaran Roberts, Yize Chen, Anna Scaglione, Sean Peisert

Volt-VAR and Volt-Watt control functions are mechanisms that are included in distributed energy resource (DER) power electronic inverters to mitigate excessively high or low voltages in distribution systems.

State-of-Charge Aware EV Charging

1 code implementation30 Nov 2021 Yize Chen, Baosen Zhang

Recent proliferation in electric vehicles (EVs) are posing profound impacts over the operation of electrical grids.

SAVER: Safe Learning-Based Controller for Real-Time Voltage Regulation

no code implementations30 Nov 2021 Yize Chen, Yuanyuan Shi, Daniel Arnold, Sean Peisert

Fast and safe voltage regulation algorithms can serve as fundamental schemes for achieving a high level of renewable penetration in the modern distribution power grids.

Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Training

no code implementations18 Oct 2021 Alexander Pan, Yongkyun Lee, huan zhang, Yize Chen, Yuanyuan Shi

Due to the proliferation of renewable energy and its intrinsic intermittency and stochasticity, current power systems face severe operational challenges.

Decision Making reinforcement-learning

Understanding the Safety Requirements for Learning-based Power Systems Operations

1 code implementation11 Oct 2021 Yize Chen, Daniel Arnold, Yuanyuan Shi, Sean Peisert

Case studies performed on both voltage regulation and topology control tasks demonstrated the potential vulnerabilities of the standard reinforcement learning algorithms, and possible measures of machine learning robustness and security are discussed for power systems operation tasks.

BIG-bench Machine Learning Decision Making +3

Forecasting Spatio-Temporal Renewable Scenarios: a Deep Generative Approach

no code implementations13 Mar 2019 Congmei Jiang, Yize Chen, Yongfang Mao, Yi Chai, Mingbiao Yu

In order to minimize the decision risks in power systems with large amount of renewable resources, there is a growing need to model the short-term generation uncertainty.

Time Series

IntelligentCrowd: Mobile Crowdsensing via Multi-Agent Reinforcement Learning

no code implementations20 Sep 2018 Yize Chen, Hao Wang

The prosperity of smart mobile devices has made mobile crowdsensing (MCS) a promising paradigm for completing complex sensing and computation tasks.

Multi-agent Reinforcement Learning reinforcement-learning

Bayesian Renewables Scenario Generation via Deep Generative Networks

1 code implementation2 Feb 2018 Yize Chen, Pan Li, Baosen Zhang

We present a method to generate renewable scenarios using Bayesian probabilities by implementing the Bayesian generative adversarial network~(Bayesian GAN), which is a variant of generative adversarial networks based on two interconnected deep neural networks.

An Unsupervised Deep Learning Approach for Scenario Forecasts

1 code implementation7 Nov 2017 Yize Chen, Xiyu Wang, Baosen Zhang

Simulation results indicate our method is able to generate scenarios that capture spatial and temporal correlations.

Optimization and Control

Model-Free Renewable Scenario Generation Using Generative Adversarial Networks

2 code implementations30 Jul 2017 Yize Chen, Yishen Wang, Daniel Kirschen, Baosen Zhang

We demonstrate that the proposed method is able to generate realistic wind and photovoltaic power profiles with full diversity of behaviors.

Blocking Transferability of Adversarial Examples in Black-Box Learning Systems

no code implementations13 Mar 2017 Hossein Hosseini, Yize Chen, Sreeram Kannan, Baosen Zhang, Radha Poovendran

Advances in Machine Learning (ML) have led to its adoption as an integral component in many applications, including banking, medical diagnosis, and driverless cars.

Medical Diagnosis

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