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Neural-network simulation of complex mini-grids


Project Description

The overall aim is to simplify simulation of a mini-grid so that it is feasible for a non-expert to design, install and maintain it successfully.

Objectives leading to this aim include:
• investigate methods to simplify and speed-up simulation of a mini-grid
• enable automatic derivation of a simulation model of a mini-grid based on obtainable data.

Research Questions

1. Can a NN-based model be learnt from an existing simulation of a complex mini-grid and how accurate is the result?
2. What NN architecture suits this best? Specifically, should dimensionality reduction approaches be used?
3. Can a NN-based model be learnt from observable (simulated) data from different parts of the mini-grid network and how does accuracy vary depending on amount of data?
4. Can a NN-based model be learnt from observable (real-world) data from different parts of the mini-grid network and how does accuracy vary depending on amount of data?

Methodology

1. Initial work will be based on existing simulation approaches to mini-grids. During this first phase, a variety of comparative simulation approaches will be tested, including NNs and other methods.
2. The second phase will involve planning the development of a mini-grid and validation of a simulation against this.
3. During the third phase real-world data will be tested to obtain an approach to self-learning of a simulation for a particular mini-grid.
The final stage will develop tools and approaches for aiding design and maintenance of a mini-grid.

Applications

Applicants must apply using the online form on the University Alliance website at https://unialliance.ac.uk/dta/cofund/how-to-apply/. Full details of the programme, eligibility details and a list of available research projects can be seen at https://unialliance.ac.uk/dta/cofund/

The final deadline for application is 12 April 2019.

Funding Notes

DTA3/COFUND participants will be employed for 36 months with a minimum salary of (approximately) £20,989 per annum. Tuition fees will waived for DTA3/COFUND participants who will also be able to access an annual DTA elective bursary to enable attendance at DTA training events and interact with colleagues across the Doctoral Training Alliance(s).
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 801604.

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