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Found 13 papers, 1 papers with code
Insights from the Use of Previously Unseen Neural Architecture Search Datasets
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 required to identify the best neural network.
A Holistic Power Optimization Approach for Microgrid Control Based on Deep Reinforcement Learning
The global energy landscape is undergoing a transformation towards decarbonization, sustainability, and cost-efficiency.
Predicting the Performance of a Computing System with Deep Networks
Existing approaches to understanding the performance of hardware largely focus around benchmarking -- leveraging standardised workloads which seek to be representative of an end-user's needs.
Analysis of Reinforcement Learning for determining task replication in workflows
We show, through simulation, that we can save 34% of the energy consumption using RL compared to a fixed number of replicas with only a 4% decrease in workflows achieving a pre-defined overhead bound.
Long-term Reproducibility for Neural Architecture Search
It is a sad reflection of modern academia that code is often ignored after publication -- there is no academic 'kudos' for bug fixes / maintenance.
Machine learning applications for electricity market agent-based models: A systematic literature review
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
A lowering in the cost of batteries and solar PV systems has led to a high uptake of solar battery home systems.
The impact of online machine-learning methods on long-term investment decisions and generator utilization in electricity markets
Through the prediction of electricity demand profile over the next 24h, we can simulate the predictions made for a day-ahead market.
Exploring market power using deep reinforcement learning for intelligent bidding strategies
If any single generator company, or a collaborating group of generator companies, control more than ${\sim}$11$\%$ of generation capacity and bid strategically, prices begin to increase by ${\sim}$25$\%$.
Deep Reinforcement Learning in Electricity Generation Investment for the Minimization of Long-Term Carbon Emissions and Electricity Costs
A change from a high-carbon emitting electricity power system to one based on renewables would aid in the mitigation of climate change.
Optimizing carbon tax for decentralized electricity markets using an agent-based model
A way to achieve this is to decarbonize the electricity grid.
Optimising energy and overhead for large parameter space simulations
A Pareto frontier can be used to identify the sets of optimal parameters for which each is the `best' for a given combination of objectives -- thus allowing decisions to be made with full knowledge.
Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments
We demonstrate, through the use of simulation, how we can reduce this wasted energy by targeting tasks at computers less likely to be needed for primary use, predicting this idle time through machine learning.
