Machine learning approach in the development of building occupant personas

• URL: http://arxiv.org/abs/2207.11239v1
• Date: Tue, 19 Jul 2022 20:27:22 GMT
• Title: Machine learning approach in the development of building occupant personas
• Authors: Sheik Murad Hassan Anik, Xinghua Gao, Na Meng
• Abstract summary: Building occupant personas are a communication tool for designers to generate a mental model that describes the archetype of users. In this study, we propose and evaluate a machine learning-based semi-automated approach to generate building occupant personas. 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.
• Score: 4.932806255841464
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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