13 citations · 33 across the 14 of their papers we have counts for
15 papers
Insights from the Use of Previously Unseen Neural Architecture Search Datasets
Rob Geada, David Towers, Matthew Forshaw +2
The boundless possibility of neural networks which can be used to solve a problem -- each with different performance -- leads to a situation where a Deep Learning expert is require…
Predicting the Performance of a Computing System with Deep Networks
Mehmet Cengiz, Matthew Forshaw, Amir Atapour-Abarghouei +1
Predicting the performance and energy consumption of computing hardware is critical for many modern applications. This will inform procurement decisions, deployment decisions, and…
Analysis of Reinforcement Learning for determining task replication in workflows
Andrew Stephen McGough, Matthew Forshaw
Executing workflows on volunteer computing resources where individual tasks may be forced to relinquish their resource for the resource's primary use leads to unpredictability and…
Long-term Reproducibility for Neural Architecture Search
David Towers, Matthew Forshaw, Amir Atapour-Abarghouei +1
It is a sad reflection of modern academia that code is often ignored after publication -- there is no academic 'kudos' for bug fixes / maintenance. Code is often unavailable or, if…
Machine learning applications for electricity market agent-based models: A systematic literature review
Alexander J. M. Kell, Stephen McGough, Matthew Forshaw
The electricity market has a vital role to play in the decarbonisation of the energy system. However, the electricity market is made up of many different variables and data inputs.…
Optimizing a domestic battery and solar photovoltaic system with deep reinforcement learning
Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw
A lowering in the cost of batteries and solar PV systems has led to a high uptake of solar battery home systems. In this work, we use the deep deterministic policy gradient algorit…
The impact of online machine-learning methods on long-term investment decisions and generator utilization in electricity markets
Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw
Electricity supply must be matched with demand at all times. This helps reduce the chances of issues such as load frequency control and the chances of electricity blackouts. To gai…
Exploring market power using deep reinforcement learning for intelligent bidding strategies
Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough
Decentralized electricity markets are often dominated by a small set of generator companies who control the majority of the capacity. In this paper, we explore the effect of the to…
Deep Reinforcement Learning in Electricity Generation Investment for the Minimization of Long-Term Carbon Emissions and Electricity Costs
Alexander J. M. Kell, Pablo Salas, Jean-Francois Mercure +2
A change from a high-carbon emitting electricity power system to one based on renewables would aid in the mitigation of climate change. Decarbonization of the electricity grid woul…
Optimizing carbon tax for decentralized electricity markets using an agent-based model
Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw
Averting the effects of anthropogenic climate change requires a transition from fossil fuels to low-carbon technology. A way to achieve this is to decarbonize the electricity grid.…
Long-term electricity market agent based model validation using genetic algorithm based optimization
Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough
Electricity market modelling is often used by governments, industry and agencies to explore the development of scenarios over differing timeframes. For example, how would the reduc…
ElecSim: Monte-Carlo Open-Source Agent-Based Model to Inform Policy for Long-Term Electricity Planning
Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough
Due to the threat of climate change, a transition from a fossil-fuel based system to one based on zero-carbon is required. However, this is not as simple as instantaneously closing…
Optimising energy and overhead for large parameter space simulations
Alexander J. M. Kell, Matthew Forshaw, A. Stephen McGough
Many systems require optimisation over multiple objectives, where objectives are characteristics of the system such as energy consumed or increase in time to perform the work. Opti…
Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments
A. Stephen McGough, Matthew Forshaw, John Brennan +2
High Throughput Computing (HTC) provides a convenient mechanism for running thousands of tasks. Many HTC systems exploit computers which are provisioned for other purposes by utili…
The Mean Drift: Tailoring the Mean Field Theory of Markov Processes for Real-World Applications
Mahmoud Talebi, Jan Friso Groote, Jean-Paul Linnartz
The statement of the mean field approximation theorem in the mean field theory of Markov processes particularly targets the behaviour of population processes with an unbounded numb…