A Hierarchical Learning System for Ambient Environmental Control of Open Plan Buildings

Perry, R.T.; Fallon, Enda; Fallon, Sheila; Qiao, Yuansong

doi:10.1109/uksim.2017.42

Cited by 1 publication

(2 citation statements)

References 9 publications

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“…This section describes the architecture of our federated artificial neural network, building on research undertaken in Perry, Fallon, Fallon, and Qiao, 2017. We illustrate how the system may be implemented to practically monitor the metrics and control the actuation of heating the ventilation controls.…”

Section: System Architecturementioning

confidence: 99%

“…The C-ANN considers the optimal ambient temperature of the entire open plan area. Having previously created a theoretical model and simulated results (Perry, Fallon, Fallon, & Qiao, 2017) this work proposes an experimentally based evaluation of the architecture using a physical scaled model. The model enables the analysis of dispersion of heat between rooms in an office environment consisting of meeting rooms and a central open plan area.…”

Section: Introductionmentioning

confidence: 99%

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A Federated Learning System for Optimised Environmental Control of Consecutive Areas

Perry

Fallon

2019

Proceedings of the International Conferences ICT, Society, and Human Beings 2019; Connected Smart Cities 2019; And Web Based Co

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Building management systems have led to artificially controlled environmental conditions. While new infrastructure will lead to a lower carbon footprint and a better working environment, the costs cannot justify the rewards at this present time. Ambient temperature regulation has the potential to mitigate excessive energy consumption. This work proposes an externally influenced environmental control Artificial Neural Network (ANN) implementation to optimise ambient temperature for a given specific area whether that be internal or external to the building. The approach uses a multi-aspect ANN. Two architectural components are introduced, an Agent ANN (A-ANN) and a Coordinating ANN (C-ANN). The Agent ANNs (A-ANN) are deployed to provide temperature control at the extremities of the open plan area. The A-ANN operates with a degree of autonomy. A Coordinating ANN (C-ANN) considers the optimal ambient temperature of the room and consequently effects the surrounding area(s). These A-ANNs have internal and external factors acting as effectors to the system such as outdoor environmental conditions and internally located effectors such as adjacent rooms. Results are presented which diagnose the effort applied by A-ANN instances in varying environmental conditions both internally and externally.

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Section: System Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%