ASSESSMENT AND OPTIMISATION OF COOLING LOADS FOR OPTIMAL BUILDING ENERGY EFFICIENCY USING GREYTAGUCHI AND ANOVA METHODS
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Amid the escalating global energy usage and carbon dioxide (CO2) emissions originating
from buildings, energy efficiency has become a topmost concern for energy policies across
various nations. The problem is further amplified by the rapid surge in the usage of air
conditioning systems, predominantly in the developing countries' infrastructure, influenced
by higher living standards, modern architectural designs, and a preference for cooler indoor
environments. The central aim of this research is to devise a cooling prediction model
utilizing Taguchi orthogonal array and ANOVA techniques to optimise cooling loads in
buildings, using Covenant University as a case study. The study primarily targets the
compelling issue of energy inefficiency in selected buildings in Covenant University, with
a special focus on improving energy efficiency through cooling load optimisation. Results
of the investigation offered a predictive model which accounted for an impressive 98.51%
of the cooling load variation, underpinned by an R2 value of 98.51% and an adjusted R2
value of 98.08%. The study further illuminated that the application of the model to the
selected buildings showcased mixed outcomes. The university library's cooling load,
originally at 137582.31W, was refined to 136816.11W, reflecting a 0.56% MAPE. The
university chapel, starting with a cooling load of 149224.61W, experienced an optimisation
down to 143776.22W, showcasing a 3.65% MAPE. Cafeteria 1 underwent a transition from
its 110380.99W to a lower 108323.48W, marking a 1.86% MAPE. For the university Guest
House, its initial cooling load of 89953.43W was pruned to 85393.19W, translating to a
5.07% MAPE. However, the Health Centre's cooling load escalated from 52494.41W to
53748.80W, resulting in a 2.39% MAPE. Further illuminating the study, the influence of
key factors on the cooling load was discerned. The area of the roof (Ra) emerged as the most
potent influence, followed closely by the number of occupants (Np), the wall area (Aw), and
the power rating of equipment (Pe). Beyond pure statistics, the exploration extended into
tangible engineering solutions conducive for energy conservation in the studied buildings.
Techniques encompassed retrofitting with energy-efficient windows, the inclusion of
dynamic building shading, optimisation of HVAC system operations, the integration of
automated lighting and energy management systems, and the contemplation of alternative
cooling mechanisms, such as evaporative cooling. Conclusively, this research not only
furthers the understanding of building energy efficiency but also furnishes a blueprint for
the effective application of energy conservation policies amidst the global urgency for
sustainable practices. The data-driven insights presented here are crucial for energy
planners, architects, and university authorities, laying a foundation for more energyefficient
building operations.
Keywords
TJ Mechanical engineering and machinery