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20182021
most citedDesign, Benchmarking and Explainability Analysis of a Game-Theoretic Framework towards Energy Efficiency in Smart Infrastructure

9 citations · 12 across the 2 of their papers we have counts for

collaborators

9 papers

cs.GT20213 cited

Selling Renewable Utilization Service to Consumers via Cloud Energy Storage

Yu Yang, Utkarsha Agwan, Guoqiang Hu +1

This paper proposes a cloud energy storage (CES) model for enabling local renewable integration of building consumers (BCs). Different from most existing third-party based ES shari…

cs.GT2020

Optimal Sharing and Fair Cost Allocation of Community Energy Storage

Yu Yang, Guoqiang Hu, Costas J. Spanos

This paper studies an energy storage (ES) sharing model which is cooperatively invested by multiple buildings for harnessing on-site renewable utilization and grid price arbitrage.…

eess.SY2020

Distributed Control of Multi-zone HVAC Systems Considering Indoor Air Quality

Yu Yang, Seshadhri Srinivasan, Guoqiang Hu +1

This paper studies a scalable control method for multi-zone heating, ventilation and air-conditioning (HVAC) systems to optimize the energy cost for maintaining thermal comfort and…

eess.SY2019

Stochastic Optimal Control of HVAC system for Energy-efficient Buildings

Yu Yang, Guoqiang Hu, Costas J. Spanos

The heating, ventilation and air-conditioning (HVAC) system accounts for substantial energy use in buildings, whereas a large group of occupants are still not actually feeling comf…

cs.LG20199 cited

Design, Benchmarking and Explainability Analysis of a Game-Theoretic Framework towards Energy Efficiency in Smart Infrastructure

Ioannis C. Konstantakopoulos, Hari Prasanna Das, Andrew R. Barkan +6

In this paper, we propose a gamification approach as a novel framework for smart building infrastructure with the goal of motivating human occupants to reconsider personal energy u…

cs.LG2019

Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms

Ruoxi Jia, David Dao, Boxin Wang +6

Given a data set containing millions of data points and a data consumer who is willing to pay for $ to train a machine learning (ML) model over , how…