Application of Principal Component Analysis (PCA) for Identifying Dominant Factors Affecting Energy Efficiency in a House

Abstract

Energy efficiency in residential buildings has become an important field of study given the increasing global energy demand and environmental concerns. Residential buildings account for the majority of global energy consumption, making it an important focus for strategies aimed at reducing carbon footprint and improving energy utilization. There are several factors that affect energy efficiency, including Relative Compactness, Surface Area, Wall Area, Roof Area, Overall Height, Orientation, Glazing Area, Glazing Area Distribution. This study aims to simplify and reduce these factors so as to obtain the dominant factors that affect the energy efficiency of residential buildings using the Principal Component Analysis (PCA) analysis method. Based on the description of the results of the study, 3 factors were obtained that most influenced, namely: The first factor (X8) is the most dominant factor with an eigenvalue of 1556.39648 . The second factor (X7) consists of an eigenvalue of 99.2431641. The third factor (X6) with an eigenvalue of 0.8. The three factors (X8 to X6) can be assumed to be the most dominant factors that affect energy efficiency in the house. So that from the eight variables analyzed using Principal Compenent Analysis (PCA), 3 variables were obtained that became the dominant factors that affect the energy efficiency of residential houses, namely Glazing Area Distribution, Glazing Area and Orientation