Publicación: PREDICTING OCCUPANT BEHAVIOR IN OFFICE BUILDINGS BASED ON THERMAL COMFORT VARIABLES USING MACHINE LEARNING
Fecha
2023
Título de la revista
ISSN de la revista
Título del volumen
Editor
ACE: ARCHITECTURE, CITY AND ENVIRONMENT = ARQUITECTURA, CIUDAD Y ENTORNO
Resumen
OFFICE WORKERS SPEND MOST OF THEIR TIME INSIDE A BUILDING, AND AS A RESULT, PHYSICAL-ENVIRONMENTAL
VARIABLES BEGIN TO PLAY A CRUCIAL ROLE IN THEIR PRODUCTIVITY AND PERFORMANCE. THIS STUDY ESTABLISHES
A CONNECTION BETWEEN MACHINE LEARNING MODELS AND THE BEHAVIOR OF OCCUPANTS AND THE SELF-
ASSESSED PRODUCTIVITY THEY EXHIBIT, THROUGH THE USE OF VARIOUS MODELS. THESE MODELS WERE
IMPLEMENTED TO IDENTIFY AND COMPARE WHICH OF THEM BETTER ESTIMATE THIS BEHAVIOR, PARTICULARLY
THE SELF-ASSESSED PRODUCTIVITY THAT INDIVIDUALS EXPERIENCE IN THEIR WORKPLACE. TO ACCOMPLISH THIS,
PHYSICAL-ENVIRONMENTAL VARIABLES, AND THE PERCEPTIONS OF OCCUPANTS FROM VARIOUS OFFICE BUILDINGS
IN THE CITY OF CONCEPCIÓN WERE COLLECTED. THIS STUDY SUCCESSFULLY COMPARES THE PERFORMANCE OF
FOUR MACHINE LEARNING MODELS (DECISION TREE, K-NEAREST NEIGHBOR, BAYESIAN MODEL, AND NEURAL
NETWORK). THEIR PERFORMANCE WAS MEASURED USING INDICATORS KNOWN AS ACCURACY, PRECISION, AND
RECALL. THESE MODELS WERE APPLIED TO BOTH AN ORIGINAL DATABASE AND A BALANCED DATABASE,
FOLLOWED BY A COMPARISON OF THE RESULTS OBTAINED. IT CAN BE ESTABLISHED THAT THERE IS A
RELATIONSHIP BETWEEN PHYSICAL-ENVIRONMENTAL VARIABLES AND THE SELF-ASSESSED PRODUCTIVITY OF
WORKERS. FURTHERMORE, IT CAN BE MENTIONED THAT THE NEURAL NETWORK IS THE MODEL THAT BEST
DESCRIBES THIS RELATIONSHIP AND, THEREFORE, ACHIEVES THE HIGHEST PERFORMANCE. THIS STUDY PROVIDES
AN APPROACH TO UNDERSTANDING OCCUPANT BEHAVIOR FROM A MACHINE LEARNING PERSPECTIVE.
Descripción
Palabras clave
productividad, máquinas de aprendizaje, edificios de oficina, confort térmico, thermal comfort, productivity, office buildings, machine learning
