Reduce Cooling Load using AI-based KNN Enhanced Roof System (KNN-ERS): CASE STUDY (TRIPOLI- LIBYA)

Abstract

Energy depletion is considered one of the greatest challenges facing the planet. One way towards solving this challenge involves architectural adaptations to the local climate to decrease energy use. This study looks at the city of Tripoli located in northern of Libya. People depend extensively on air conditioning systems that result in higher energy consumption. This study proposes implementing AI-based KNN Enhanced Roof System (KNN-ERS) in buildings to decrease cooling energy consumption. The focus is on the importance of insulating the external walls of the building, by adding 50.8 mm thick board insulation to the wall components and use of double glazing techniques. Our methodology consisted of an energy simulation using a hourly analysis program (HAP). Using this simulation, we assessed the effects of KNN-ERS on the reduction rate of cooling loads in library building at the Higher Institute for Science and Technology Qasr Bin Ghashir, Tripoli, Libya. Simulation results show that the proposed AI-based KNN Enhanced Roof System (KNN-ERS) reduce the cooling load significantly, from 32,242 kW to approximately 30,580 kW during peak cooling load in July. This represents a 5.15% reduction in the total cooling load. Insulating the external walls reduces the cooling load through the walls by 32%, and using double glazing technology reduces the thermal load through the windows by 33.7%. The significance of this impact suggests that architects should be more mindful about utilizing passive cooling methods in buildings, reducing the consumption of energy for residents and prompt accomplishing environmental friendly buildings.