Abstract
| Original language | English |
|---|---|
| Title of host publication | ENERGY 2014 : The Fourth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies |
| Publisher | International Academy, Research, and Industry Association |
| Pages | 63-68 |
| Number of pages | 6 |
| ISBN (Electronic) | 978-1-61208-332-2 |
| Publication status | Published - 24 Apr 2014 |
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Keywords
- random neural network
- building temperature
- indoor environment
Cite this
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Analysis of novel random neural network controller for residential building temperature control. / Javed, Abbas; Larijani, Hadi; Ahmadinia, Ali; Emmanuel, Rohinton.
ENERGY 2014 : The Fourth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies. International Academy, Research, and Industry Association, 2014. p. 63-68.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Analysis of novel random neural network controller for residential building temperature control
AU - Javed, Abbas
AU - Larijani, Hadi
AU - Ahmadinia, Ali
AU - Emmanuel, Rohinton
PY - 2014/4/24
Y1 - 2014/4/24
N2 - Random neural networks (RNN) have strong generalisation capabilities and are easy to implement on hardware as compared to Artificial Neural Networks (ANN). In this paper, a novel RNN controller is proposed to maintain a comfortable indoor environment in a single zone residential building fitted with radiators for heating. This controller is capable of maintaining a comfortable indoor environment on the basis of a predicted mean vote (PMV)-based set point. The implemented RNN controller is compared with ANN controller for energy consumption, indoor room temperature, and minimum square error. Results show that for same training data and learning algorithm parameters, RNN converges faster and it consumes less energy, results in better comfortable room temperature as compared to ANN controller.
AB - Random neural networks (RNN) have strong generalisation capabilities and are easy to implement on hardware as compared to Artificial Neural Networks (ANN). In this paper, a novel RNN controller is proposed to maintain a comfortable indoor environment in a single zone residential building fitted with radiators for heating. This controller is capable of maintaining a comfortable indoor environment on the basis of a predicted mean vote (PMV)-based set point. The implemented RNN controller is compared with ANN controller for energy consumption, indoor room temperature, and minimum square error. Results show that for same training data and learning algorithm parameters, RNN converges faster and it consumes less energy, results in better comfortable room temperature as compared to ANN controller.
KW - random neural network
KW - building temperature
KW - indoor environment
M3 - Conference contribution
SP - 63
EP - 68
BT - ENERGY 2014 : The Fourth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies
PB - International Academy, Research, and Industry Association
ER -