MODELLING ROOM COOLING CAPACITY WITH FUZZY LOGIC PROCEDURE

HO Adeyemi; ZOO Jagun; SO Giwa; OR Adetunji; RA Owamoyo

doi:10.4314/njt.353.1127

MODELLING ROOM COOLING CAPACITY WITH FUZZY LOGIC PROCEDURE

Authors

HO Adeyemi DEPT. OF AGRICULTURAL AND MECHANICAL ENGR., OLABISI ONABANJO UNIV., IBOGUN CAMPUS, OGUN STATE. NIGERIA
ZOO Jagun DEPT. OF ELECTRICAL AND COMPUTER ENGR., OLABISI ONABANJO UNIVERSITY, IBOGUN CAMPUS, OGUN STATE. NIGERIA
SO Giwa DEPT. OF AGRICULTURAL AND MECHANICAL ENGR., OLABISI ONABANJO UNIV., IBOGUN CAMPUS, OGUN STATE. NIGERIA
OR Adetunji DEPT. OF MECHANICAL ENGINEERING, FEDERAL UNIVERSITY OF AGRICULTURE, ABEOKUTA, OGUN STATE. NIGERIA
RA Owamoyo DIRECTORATE OF WORKS AND SERVICES, FEDERAL COLLEGE OF EDUCATION, OSIELE, ABEOKUTA, OGUN STATE. NIGERIA

DOI:

https://doi.org/10.4314/njt.353.1127

Keywords:

Fuzzy logic, cooling capacity, residential rooms, input variable

Abstract

The primary aim of this study is to develop a model for estimation of the cooling requirement of residential rooms. Fuzzy logic was employed to model four input variables (window area (m²), roof area (m²), external wall area (m²) and internal load (Watt). The algorithm of the inference engine applied sets of 81 linguistic rules to generate the output variable in Cooling Load rating. A paired t-test was carried out using SPSS version 20 package, with the results of human professionals’ calculations for each assessed rooms compared with the model generated Cooling Load capacities for the same set of variables. The human calculation and model results were observed to be strongly correlated (r=0.880, p<0.001) with no significant difference between the two sets of variables (t₁₉=-1.697, p>0.001). On the average, human calculation values were 221.5 points lower than model calculated values (at 95% confidence interval [-447.55, 4.50]). The study proposed a model to size the cooling capacity of residential rooms. The model is capable of providing results comparable to that of human professionals in the application area. It is simple and can find its usefulness among building consultants/professionals and home owners.

http://dx.doi.org/10.4314/njt.v35i3.7

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Issue

Vol. 35 No. 3 (2016)

Section

Chemical, Industrial, Materials, Mechanical, Metallurgical, Petroleum & Production Engineering

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How to Cite

MODELLING ROOM COOLING CAPACITY WITH FUZZY LOGIC PROCEDURE. (2016). Nigerian Journal of Technology, 35(3), 519-526. https://doi.org/10.4314/njt.353.1127