Machine learning approach in the development of building occupant personas

TL;DR

This paper presents a machine learning-based semi-automated method to develop building occupant personas, improving efficiency and accuracy in understanding occupant preferences for smarter building design.

Contribution

It introduces a novel semi-automated approach using multiple machine learning techniques to predict occupant characteristics, reducing manual data analysis efforts.

Findings

Models achieved an average accuracy of 61%.

Over 90% accuracy for key occupant attributes.

Demonstrated feasibility of machine learning in persona development.

Abstract

The user persona is a communication tool for designers to generate a mental model that describes the archetype of users. Developing building occupant personas is proven to be an effective method for human-centered smart building design, which considers occupant comfort, behavior, and energy consumption. Optimization of building energy consumption also requires a deep understanding of occupants' preferences and behaviors. The current approaches to developing building occupant personas face a major obstruction of manual data processing and analysis. In this study, we propose and evaluate a machine learning-based semi-automated approach to generate building occupant personas. We investigate the 2015 Residential Energy Consumption Dataset with five machine learning techniques - Linear Discriminant Analysis, K Nearest Neighbors, Decision Tree (Random Forest), Support Vector Machine, and AdaBoost classifier - for the prediction of 16 occupant characteristics, such as age, education, and, thermal comfort. The models achieve an average accuracy of 61% and accuracy over 90% for attributes including the number of occupants in the household, their age group, and preferred usage of heating or cooling equipment. The results of the study show the feasibility of using machine learning techniques for the development of building occupant persona to minimize human effort.