Abstract
Deep learning has attracted considerable attention across multiple application domains, including computer vision, signal processing and natural language processing. Although quite a few single node deep learning frameworks exist, such as tensorflow, pytorch and keras, we still lack a complete processing structure that can accommodate large scale data processing, version control, and deployment, all while staying agnostic of any specific single node framework. To bridge this gap, this paper proposes a new, higher level framework, i.e. Nemesyst, which uses databases along with model sequentialisation to allow processes to be fed unique and transformed data at the point of need. This facilitates near real-time application and makes models available for further training or use at any node that has access to the database simultaneously. Nemesyst is well suited as an application framework for internet of things aggregated control systems, deploying deep learning techniques to optimise individual machines in massive networks. To demonstrate this framework, we adopted a case study in a novel domain; deploying deep learning to optimise the high speed control of electrical power consumed by a massive internet of things network of retail refrigeration systems in proportion to load available on the UK National Grid (a demand side response). The case study demonstrated for the first time in such a setting how deep learning models, such as Recurrent Neural Networks (vanilla and Long-Short-Term Memory) and Generative Adversarial Networks paired with Nemesyst, achieve compelling performance, whilst still being malleable to future adjustments as both the data and requirements inevitably change over time.
Original language | English |
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Article number | 103133 |
Number of pages | 11 |
Journal | Computers in Industry |
Volume | 113 |
Early online date | 11 Oct 2019 |
DOIs | |
Publication status | Published - Dec 2019 |
Bibliographical note
This research was supported by the Innovate UK grant “The development of dynamic energy control mechanisms for food retailing refrigeration systems” with a reference number 102626. We would also like to thank our partners Intelligent Maintenance Systems Limited (IMS), the Grimsby Institute (GIFHE) and Tesco Stores Limited for their support throughout this project.Keywords
- Databases
- Deep learning
- Demand side response
- Distributed computing
- Internet of things
- Parallel computing
- Refrigeration