DocumentCode :
2938842
Title :
Improving a neural networks based HVAC predictive control approach
Author :
Ruano, A.E. ; Silva, S. ; Pesteh, S. ; Ferreira, P.M. ; Duarte, H. ; Mestre, G. ; Khosravani, H. ; Horta, R.
Author_Institution :
Univ. of Algarve, Faro, Portugal
fYear :
2015
fDate :
15-17 May 2015
Firstpage :
1
Lastpage :
6
Abstract :
This paper improves an existing Model Based Predictive Control Approach (MBPC), applied for Heating Ventilation and Air Conditioning (HVAC) control in buildings. The existing approach uses the Predictive Mean Vote (PMV) to assess thermal comfort. It has been found that PMV estimation and forecasts deteriorate when the room is occupied. In order to solve this problem, we propose to incorporate measurements of activity inside the room in the predictive models of the inside air temperature. Another improvement to the existing approach is to use an economic cost function, reflecting the money needed for the HVAC control, instead of a cost function related with the consumption of energy.
Keywords :
HVAC; building management systems; energy consumption; energy management systems; neurocontrollers; predictive control; MBPC; PMV estimation; PMV forecasting; activity measurements; buildings; economic cost function; energy consumption; heating ventilation-and-air conditioning control; inside air temperature; model-based predictive control approach improvement; neural network-based HVAC predictive control approach improvement; occupied room; predictive mean vote; thermal comfort assessment; Computational modeling; Cost function; Data models; Economics; Estimation; Heating; Predictive models; Economic Cost Function; HVAC Predictive Control; Multi-Objective Genetic Algorithm; Neural Networks; Predicted Mean Vote; Room Occupancy; Thermal Comfort;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Signal Processing (WISP), 2015 IEEE 9th International Symposium on
Conference_Location :
Siena
Type :
conf
DOI :
10.1109/WISP.2015.7139168
Filename :
7139168
Link To Document :
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