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Title:
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A FEDERATED LEARNING SYSTEM FOR OPTIMISED
ENVIRONMENTAL CONTROL OF CONSECUTIVE AREAS |
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Author(s):
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Robert Perry and Enda Fallon |
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ISBN:
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978-989-8533-90-6 |
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Editors:
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Piet Kommers and Guo Chao Peng |
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Year:
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2019 |
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Edition:
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Single |
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Keywords:
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Artificial Neural Networks, Ambient Temperature Control, Building Management Systems |
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Type:
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Full Paper |
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First Page:
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209 |
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Last Page:
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216 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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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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