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ORCIDA. Stephen McGough
- > Home > Persons > A. Stephen McGough
Publications
- 2023
[c77]Mohamad Elhadi Abushofa
, Amir Atapour Abarghouei, Matthew Forshaw, Andrew Stephen McGough:
FEGR: Feature Enhanced Graph Representation Method for Graph Classification. ASONAM 2023: 371-378- [c71]Mehmet Cengiz, Matthew Forshaw, Amir Atapour-Abarghouei, Andrew Stephen McGough:
Predicting the Performance of a Computing System with Deep Networks. ICPE 2023: 91-98 - [i39]Mehmet Cengiz, Matthew Forshaw, Amir Atapour-Abarghouei, Andrew Stephen McGough:
Predicting the Performance of a Computing System with Deep Networks. CoRR abs/2302.13638 (2023) - 2022
- [c67]Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw:
Optimizing a domestic battery and solar photovoltaic system with deep reinforcement learning. IEEE Big Data 2022: 4495-4502 - [c66]Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw:
A systematic literature review on machine learning for electricity market agent-based models. IEEE Big Data 2022: 4503-4512 - [c64]Andrew Stephen McGough, Matthew Forshaw:
Analysis of Reinforcement Learning for Determining Task Replication in Workflows. EPEW 2022: 117-132 - [i32]Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw:
Machine learning applications for electricity market agent-based models: A systematic literature review. CoRR abs/2206.02196 (2022) - [i31]David Towers, Matthew Forshaw, Amir Atapour-Abarghouei, Andrew Stephen McGough:
Long-term Reproducibility for Neural Architecture Search. CoRR abs/2207.04821 (2022) - [i30]Andrew Stephen McGough, Matthew Forshaw:
Analysis of Reinforcement Learning for determining task replication in workflows. CoRR abs/2209.13531 (2022) - 2021
- [j20]Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw:
The impact of online machine-learning methods on long-term investment decisions and generator utilization in electricity markets. Sustain. Comput. Informatics Syst. 30: 100532 (2021) - [i25]Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw:
The impact of online machine-learning methods on long-term investment decisions and generator utilization in electricity markets. CoRR abs/2103.04327 (2021) - [i24]Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw:
Optimizing a domestic battery and solar photovoltaic system with deep reinforcement learning. CoRR abs/2109.05024 (2021) - 2020
- [c58]Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough:
Exploring market power using deep reinforcement learning for intelligent bidding strategies. IEEE BigData 2020: 4402-4411 - [c57]Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough:
Long-term electricity market agent based model validation using genetic algorithm based optimization. e-Energy 2020: 1-13 - [c56]Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw:
Optimizing carbon tax for decentralized electricity markets using an agent-based model. e-Energy 2020: 454-460 - [i22]Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough:
Long-term electricity market agent based model validation using genetic algorithm based optimization. CoRR abs/2005.10346 (2020) - [i20]Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw:
Optimizing carbon tax for decentralized electricity markets using an agent-based model. CoRR abs/2006.01601 (2020) - [i16]Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough:
Exploring market power using deep reinforcement learning for intelligent bidding strategies. CoRR abs/2011.04079 (2020) - 2019
- [c50]Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough:
Modelling Carbon Tax in the UK Electricity Market using an Agent-Based Model. e-Energy 2019: 425-427 - [c49]Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough:
ElecSim: Monte-Carlo Open-Source Agent-Based Model to Inform Policy for Long-Term Electricity Planning. e-Energy 2019: 556-565 - [c48]Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough:
Optimising energy and overhead for large parameter space simulations. IGSC 2019: 1-8 - [i10]Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough:
Optimising energy and overhead for large parameter space simulations. CoRR abs/1910.02516 (2019) - [i9]Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough:
ElecSim: Monte-Carlo Open-Source Agent-Based Model to Inform Policy for Long-Term Electricity Planning. CoRR abs/1911.01203 (2019) - 2018
- [j16]A. Stephen McGough, Matthew Forshaw:
Introduction to special issue on Energy-Aware Simulation and Modelling (ENERGY-SIM). Sustain. Comput. Informatics Syst. 18: 135-136 (2018) - [c43]Osama Nasser Alrajeh, Matthew Forshaw, Andrew Stephen McGough, Nigel Thomas:
Simulation of Virtual Machine Live Migration in High Throughput Computing Environments. DS-RT 2018: 47-54 - [c42]Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw:
Segmenting Residential Smart Meter Data for Short-Term Load Forecasting. e-Energy 2018: 91-96 - [c41]A. Stephen McGough, Matthew Forshaw, John Brennan, Noura Al Moubayed, Stephen Bonner:
Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments. IGSC 2018: 1-8 - [c36]A. Stephen McGough, Matthew Forshaw:
Evaluation of Energy Consumption of Replicated Tasks in a Volunteer Computing Environment. ICPE Companion 2018: 85-90 - [i5]A. Stephen McGough, Matthew Forshaw, John Brennan, Noura Al Moubayed, Stephen Bonner:
Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments. CoRR abs/1810.08675 (2018) - 2017
- [c32]A. Stephen McGough, Noura Al Moubayed, Matthew Forshaw:
Using Machine Learning in Trace-driven Energy-Aware Simulations of High-Throughput Computing Systems. ICPE Companion 2017: 55-60 - 2016
- [j15]Matthew Forshaw, A. Stephen McGough, Nigel Thomas:
HTC-Sim: a trace-driven simulation framework for energy consumption in high-throughput computing systems. Concurr. Comput. Pract. Exp. 28(12): 3260-3290 (2016) - 2015
- [c26]A. Stephen McGough, Matthew Forshaw:
Energy-Aware Simulation of Workflow Execution in High Throughput Computing Systems. DS-RT 2015: 25-32 - [c25]Matthew Forshaw, A. Stephen McGough:
Flipping the priority: effects of prioritising HTC jobs on energy consumption in a multi-use cluster. SimuTools 2015: 357-364 - 2014
- [j13]Andrew Stephen McGough, Matthew Forshaw, Clive Gerrard, Stuart Wheater, Ben Allen, Paul Robinson:
Comparison of a cost-effective virtual cloud cluster with an existing campus cluster. Future Gener. Comput. Syst. 41: 65-78 (2014) - [j12]A. Stephen McGough, Matthew Forshaw:
Reduction of wasted energy in a volunteer computing system through Reinforcement Learning. Sustain. Comput. Informatics Syst. 4(4): 262-275 (2014) - [c24]Matthew Forshaw, Nigel Thomas, A. Stephen McGough:
Trace-Driven Simulation for Energy Consumption in High Throughput Computing Systems. DS-RT 2014: 27-34 - [c22]Matthew Forshaw, Andrew Stephen McGough, Nigel Thomas:
On Energy-efficient Checkpointing in High-throughput Cycle-stealing Distributed Systems. SMARTGREENS 2014: 262-267 - [c21]Matthew Forshaw, Andrew Stephen McGough, Nigel Thomas:
Energy-efficient Checkpointing in High-throughput Cycle-stealing Distributed Systems. PASM 2014: 65-90 - 2013
- [j11]Andrew Stephen McGough, Matthew Forshaw, Clive Gerrard, Paul Robinson, Stuart Wheater:
Analysis of power-saving techniques over a large multi-use cluster with variable workload. Concurr. Comput. Pract. Exp. 25(18): 2501-2522 (2013) - 2012
- [c20]Andrew Stephen McGough, Matthew Forshaw, Clive Gerrard, Stuart Wheater:
Reducing the Number of Miscreant Tasks Executions in a Multi-use Cluster. CGC 2012: 296-303
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