Abstract
environment in double storey residential building by controlling the motorised thermostatic radiator valves
(TRVs) mounted on radiators. In order to monitor the indoor environmental condition of the building the
sensor interface collects information from different sensors and sends this information to random neural
network (RNN) controller. The RNN controller ensures comfortable environment for occupants by
regulating the air temperature of the building according to the setpoint suggested by PMV index based
variable setpoint estimator. The proposed RNN controller is compared with ANN controller and it is found
that accuracy of RNN controller is 26% more than ANN controller and conserves 2.75% more energy than
ANN controller at PMV index based temperature setpoints. The RNN controller has the capability to adjust
the room temperature to lower setpoints (not included in the training data) while ANN controller failed to
maintain accurate comfortable environment for the operating points not covered in the training data. The
results show that the percentage of accurate air temperature regulation for RNN controller is 95.69% while
for ANN it is 2.22%.
| Original language | English |
|---|---|
| Title of host publication | Proceeding of iiSBE Net Zero Built Environment 2014 17th Rinker International Conference, Gainesville, FL, 6&7 March |
| Pages | 326-343 |
| Number of pages | 18 |
| Publication status | Published - 6 Mar 2014 |
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Keywords
- random neural network, artificial neural network, Building Energy Management Systems,
- artificial neural network
- PMV index based control scheme
- building energy management systems
Cite this
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Intelligent RNN controller for domestic (residential) heating system. / Javed, Abbas; Larijani, Hadi; Ahmadinia, Ali; Emmanuel, Rohinton.
Proceeding of iiSBE Net Zero Built Environment 2014 17th Rinker International Conference, Gainesville, FL, 6&7 March. 2014. p. 326-343.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Intelligent RNN controller for domestic (residential) heating system
AU - Javed, Abbas
AU - Larijani, Hadi
AU - Ahmadinia, Ali
AU - Emmanuel, Rohinton
PY - 2014/3/6
Y1 - 2014/3/6
N2 - In this paper, we propose a novel variable setpoint RNN controller for maintaining comfortable indoor environment in double storey residential building by controlling the motorised thermostatic radiator valves (TRVs) mounted on radiators. In order to monitor the indoor environmental condition of the building the sensor interface collects information from different sensors and sends this information to random neural network (RNN) controller. The RNN controller ensures comfortable environment for occupants by regulating the air temperature of the building according to the setpoint suggested by PMV index based variable setpoint estimator. The proposed RNN controller is compared with ANN controller and it is found that accuracy of RNN controller is 26% more than ANN controller and conserves 2.75% more energy than ANN controller at PMV index based temperature setpoints. The RNN controller has the capability to adjust the room temperature to lower setpoints (not included in the training data) while ANN controller failed to maintain accurate comfortable environment for the operating points not covered in the training data. The results show that the percentage of accurate air temperature regulation for RNN controller is 95.69% while for ANN it is 2.22%.
AB - In this paper, we propose a novel variable setpoint RNN controller for maintaining comfortable indoor environment in double storey residential building by controlling the motorised thermostatic radiator valves (TRVs) mounted on radiators. In order to monitor the indoor environmental condition of the building the sensor interface collects information from different sensors and sends this information to random neural network (RNN) controller. The RNN controller ensures comfortable environment for occupants by regulating the air temperature of the building according to the setpoint suggested by PMV index based variable setpoint estimator. The proposed RNN controller is compared with ANN controller and it is found that accuracy of RNN controller is 26% more than ANN controller and conserves 2.75% more energy than ANN controller at PMV index based temperature setpoints. The RNN controller has the capability to adjust the room temperature to lower setpoints (not included in the training data) while ANN controller failed to maintain accurate comfortable environment for the operating points not covered in the training data. The results show that the percentage of accurate air temperature regulation for RNN controller is 95.69% while for ANN it is 2.22%.
KW - random neural network, artificial neural network, Building Energy Management Systems,
KW - artificial neural network
KW - PMV index based control scheme
KW - building energy management systems
M3 - Conference contribution
SP - 326
EP - 343
BT - Proceeding of iiSBE Net Zero Built Environment 2014 17th Rinker International Conference, Gainesville, FL, 6&7 March
ER -