Folgen
Ivana Dusparic
Ivana Dusparic
Associate Professor, School of Computer Science and Statistics, Trinity College Dublin
Bestätigte E-Mail-Adresse bei scss.tcd.ie - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Residential Electrical Demand Forecasting in Very Small Scale: An Evaluation of Forecasting Methods
A Marinescu, C Harris, I Dusparic, S Clarke, V Cahill
882013
The transition to autonomous cars, the redesign of cities and the future of urban sustainability
F Cugurullo, RA Acheampong, M Guériau, I Dusparic
Urban Geography, 2020
622020
Multi-agent residential demand response based on load forecasting
I Dusparic, C Harris, A Marinescu, V Cahill, S Clarke
2013 1st IEEE conference on technologies for sustainability (SusTech), 90-96, 2013
622013
Can autonomous vehicles enable sustainable mobility in future cities? Insights and policy challenges from user preferences over different urban transport options
RA Acheampong, F Cugurullo, M Gueriau, I Dusparic
Cities 112, 103134, 2021
592021
Distributed W-Learning: Multi-policy optimization in self-organizing systems
I Dusparic, V Cahill
Self-Adaptive and Self-Organizing Systems, 2009. SASO'09. Third IEEE …, 2009
542009
SAMoD: Shared Autonomous Mobility-on-Demand using Decentralized Reinforcement Learning
M Gueriau, I Dusparic
The 21st IEEE International Conference on Intelligent Transportation Systems …, 2018
482018
Accelerating Learning in Multi-Objective Systems through Transfer Learning
A Taylor, I Dusparic, E Galván-López, S Clarke, V Cahill
In a Special Session on Learning and Optimization in Multi-Criteria Dynamic …, 2014
482014
Prediction-Based Multi-Agent Reinforcement Learning in Inherently Non-Stationary Environments
A Marinescu, I Dusparic, S Clarke
ACM Transactions on Autonomous and Adaptive Systems (TAAS) 12 (2), 9, 2017
472017
Autonomic multi-policy optimization in pervasive systems: Overview and evaluation
I Dusparic, V Cahill
ACM Transactions on Autonomous and Adaptive Systems (TAAS) 7 (1), 1-25, 2012
422012
Residential demand response: Experimental evaluation and comparison of self-organizing techniques
I Dusparic, A Taylor, A Marinescu, F Golpayegani, S Clarke
Renewable and Sustainable Energy Reviews 80, 1528-1536, 2017
362017
Transfer Learning in Multi-Agent Systems Through Parallel Transfer
A Taylor, I Dusparic, E Galván-López, S Clarke, V Cahill
362013
Shared Autonomous Mobility-on-Demand: Learning-based approach and its performance in the presence of traffic congestion
M Gueriau, F Cugurullo, RA Acheampong, I Dusparic
IEEE Intelligent Transportation Systems Magazine, 2020
342020
Heterogeneous Multi-Agent Deep Reinforcement Learning for Traffic Lights Control.
JA Calvo, I Dusparic
AICS, 2-13, 2018
342018
Maximizing Renewable Energy Use with Decentralized Residential Demand Response
I Dusparic, A Taylor, A Marinescu, V Cahill, S Clarke
The First IEEE International Smart Cities Conference (ISC2-2015), 2015
312015
Multi-agent deep reinforcement learning for zero energy communities
A Prasad, I Dusparic
IEEE ISGT PES Europe 2019, 2019
292019
Towards Autonomic Urban Traffic Control with Collaborative Multi-Policy Reinforcement Learning
I Dusparic, J Monteil, V Cahill
IEEE 19th International Conference on Intelligent Transportation Systems …, 2016
282016
Quantifying the impact of connected and autonomous vehicles on traffic efficiency and safety in mixed traffic
M Gueriau, I Dusparic
23rd IEEE International Conference on Intelligent Transportation Systems …, 2020
242020
A hybrid approach to very small scale electrical demand forecasting
A Marinescu, C Harris, I Dusparic, V Cahill, S Clarke
ISGT 2014, 1-5, 2014
242014
Multi-agent collaboration for conflict management in residential demand response
F Golpayegani, I Dusparic, A Taylor, S Clarke
Computer Communications 96, 63-72, 2016
222016
Set point control for charging of electric vehicles on the distribution network
C Harris, I Dusparic, E Galván-López, A Marinescu, V Cahill, S Clarke
2014 IEEE Power & Energy Society Innovative Smart Grid Technologies …, 2014
202014
Das System kann den Vorgang jetzt nicht ausführen. Versuchen Sie es später erneut.
Artikel 1–20