TY - CHAP
T1 - Application of soft-computing techniques in modelling of buildings
AU - Azzi, Djamel
AU - Gegov, Alexander Emilov
AU - Virk, G.
AU - Haynes, B.
AU - Alkadhimi, Khalil Ibrahim Hady
PY - 2001
Y1 - 2001
N2 - The paper presents recent results on the application of soft computing techniques for predictive modelling in the built sector. More specifically, an air-conditioned zone (Anglesea Building, University of Portsmouth), a naturally ventilated room (Portland Building, University of Portsmouth), and an endothermic building (St Catherine’s Lighthouse, Isle of Wight) are considered. The zones are subjected to occupancy effects and external disturbances which are difficult to predict in a quantitative way and hence the soft computing approach seems to be a better alternative. In fact, the overall complexity of the problem domain makes the modelling of the internal climate in buildings a difficult task which is not always carried out in a satisfactory way by traditional deterministic and stochastic methods. The approach adopted uses fuzzy logic for modelling, as well as neural networks for adaptation and genetic algorithms for optimisation of the fuzzy model. The latter is of the Takagi-Sugeno type and it is built by subtractive clustering as a result of which the initial values of the antecedent non-linear membership functions and the consequent linear algebraic equations parameters are determined. A method of a combinatorial search over all possible fuzzy model structures for a specified plant order is presented. The model parameters are further adjusted by a back-propagation neural network and a real-valued genetic algorithm in order to obtain a better fit to the measured data. Modelling results with actual data from the three buildings are presented where the initial (fuzzy) and the final (fuzzy-neuro and fuzzy-genetic) models are shown.
AB - The paper presents recent results on the application of soft computing techniques for predictive modelling in the built sector. More specifically, an air-conditioned zone (Anglesea Building, University of Portsmouth), a naturally ventilated room (Portland Building, University of Portsmouth), and an endothermic building (St Catherine’s Lighthouse, Isle of Wight) are considered. The zones are subjected to occupancy effects and external disturbances which are difficult to predict in a quantitative way and hence the soft computing approach seems to be a better alternative. In fact, the overall complexity of the problem domain makes the modelling of the internal climate in buildings a difficult task which is not always carried out in a satisfactory way by traditional deterministic and stochastic methods. The approach adopted uses fuzzy logic for modelling, as well as neural networks for adaptation and genetic algorithms for optimisation of the fuzzy model. The latter is of the Takagi-Sugeno type and it is built by subtractive clustering as a result of which the initial values of the antecedent non-linear membership functions and the consequent linear algebraic equations parameters are determined. A method of a combinatorial search over all possible fuzzy model structures for a specified plant order is presented. The model parameters are further adjusted by a back-propagation neural network and a real-valued genetic algorithm in order to obtain a better fit to the measured data. Modelling results with actual data from the three buildings are presented where the initial (fuzzy) and the final (fuzzy-neuro and fuzzy-genetic) models are shown.
KW - soft-computing
KW - intelligent modeling
KW - building management systems
U2 - 10.1007/978-3-7908-1829-1_17
DO - 10.1007/978-3-7908-1829-1_17
M3 - Chapter (peer-reviewed)
SN - 9783790813616
VL - 2
T3 - Advances in Soft Computing
SP - 143
EP - 150
BT - Developments in soft computing
A2 - John, Robert
A2 - Birkenhead, Ralph
PB - Springer
CY - Heidelberg
ER -