Construyendo edificios más inteligentes: lo que hemos aprendido ...
Estamos en una época dorada en la construcción de edificios. La integración de datos entre sistemas de construcción permite crear un ...
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A partir de los resultados de los estudios de los usuarios, <anchor>desarrollar perfiles</anchor> ayudaría a representar los distintos segmentos del público objetivo del edificio. Cada perfil debe encapsular las características clave, los objetivos, las motivaciones y los retos de los habitantes a los que representa. Esto ha ayudado a las partes interesadas a empatizar con las diversas necesidades de los usuarios del edificio y a adaptar las soluciones de diseño en consecuencia.
Towards automated occupant profile creation in smart buildings: A machine learning-enabled approach for user persona generation - ScienceDirect
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## Energy and Buildings

# Towards automated occupant profile creation in smart buildings: A machine learning-enabled approach for user persona generation

Author links open overlay panelSheik Murad HassanAnika, XinghuaGaob, NaMenga

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## Highlights

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  A machine learning-based approach for automated occupant profile creation.

- •
  Residential Energy Consumption Dataset is used to develop models.

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  Six machine learning techniques are used to predict 16 occupant characteristics.

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  Large Language Model, ChatGPT, is used to generate occupant personas.

## 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. This study proposes a machine learning-based approach for occupant characteristics classification and prediction with a view toward partially automating the building occupant persona generation process. It investigates the 2015 Residential Energy Consumption Dataset with six 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 achieved moderate accuracy in predicting most of the occupant characteristics and significantly higher accuracy (over 90%) for attributes including the number of occupants in the household, their age group, and preferred usage of primary cooling equipment. The results of the study show the feasibility of using machine learning techniques for occupant characteristics prediction and automating the development of building occupant persona to minimize human effort.

## Introduction

People spend most of the time indoors [1], and the impact of a building on its occupants is significant [2]. Smart Buildings are buildings that integrate intelligence, enterprise, control, materials, and construction as an entire building system, with adaptability, not reactivity, at its core, to meet the drivers for building progression: energy and efficiency, longevity, comfort, and, satisfaction [3]. In recent years, human-centeredness has become an increasingly important factor for smart building design and operation. Optimizing building performance requires a deep understanding of occupants’ behavior and preferences. Developing building occupant personas is proven to be effective in creating occupant profiles for human-centered smart buildings [4]. An occupant persona is a tool of communication for designers to develop a conceptual model that describes the archetype of occupants [5]. The development of a building occupant persona can assist the building designers, developers, and, engineers in accurately estimating resource demand through realistic and representative occupant profiles, enabling pragmatic occupant-centric building design and operation. The increasing availability of data from a wide range of sources will allow smart buildings to become adaptable, and prepare themselves for context and change over all timescales [3].

One of the key objectives of smart buildings is to maximize occupant comfort while minimizing energy consumption [6]. Buildings and urban spaces increasingly incorporate artificial intelligence and new forms of interactivity, raising a wide span of research questions about the future of human experiences with, and within, built environments. This emerging area is defined as Human-Building Interaction (HBI) [7]. HBI affects human wellbeing and the surrounding environment. Although energy and technology are placed into buildings by people for their own comfort and recreation, often the decision-making process excludes the human factor from the design itself. Smart buildings provide an opportunity to consider the physical, physiological, and, psychological needs of occupants in the building design. Human-centered design (HCD) places humans at the center of the building design, which can accelerate smart housing design for people. To maximize human well-being and the optimize performance of smart buildings, an iterative, human-centered approach to building design must be employed.

Agee et al. [4] introduced a human-centered approach to smart housing, leading to the development of data-driven smart housing personas that communicate smart housing user needs. In their work, the authors utilized descriptive statistics and behavioral analysis to describe the physical, physiological, and psychological needs of the occupants. Personas help designers anchor their work in a fictional user’s needs in the design of products and systems [8]. Their proposed approach can reduce the risk of designers designing for themselves.

Developing building occupant personas benefits both the occupants and building designers in many ways. A user-centric building design can help understand the needs, behaviors, and preferences of the potential occupants, building designers can create spaces that are tailored to the users’ needs, leading to a more user-centered design approach. By designing buildings with the occupant persona in mind, the building can provide a more pleasant and satisfying experience for the occupants, leading to higher levels of user satisfaction and productivity.

Designing buildings that meet the needs of the occupant persona can lead to improved building performance, such as energy efficiency and indoor air quality, as the building is designed to meet the specific needs of its occupants. It can also reduce the need for retrofits and renovations, resulting in cost savings over the building’s lifecycle. Understanding the needs and preferences of potential occupants can help building-owners and developers to better market and lease the building, leading to higher occupancy rates and increased revenue.

However, the task faces multiple challenges beginning with the limited availability of building occupant data. The lack of data related to occupants and their living environment is one of the major pullbacks in smart housing persona development research. Even with the limited data that is available, the process of developing building occupant persona is manual and time-consuming.

Currently, the occupant persona development process requires researchers manually analyzing data, conducting interviews, clustering the occupant profiles based on multiple criteria, and finally constructing a persona [4]. The entire process takes much time because it involves manual labor in all the steps. Automating a single step of this procedure can accelerate the entire persona development task. This work tries to address the gap of accelerating the tedious and time consumption process of occupant persona development by incorporating machine learning tools in the process of occupant characteristics classification and prediction. Related studies in the domain indicate that the characterization of occupant behavior using machine learning tools on energy usage data has not been studied yet. Moreover, although there is a scarcity of residential building data, there has been some work to address this issue recently. For example, Anik et al. [9] presented a cost-effective and portable framework for indoor data collection, and Song, et al. [10] discussed data collection and analysis methods for analyzing human comfort in the indoor environment. With more building data available, and the development of machine learning technologies, new opportunities are emerging. Machine learning is an automated process that extracts patterns from data [11]. In the field of predictive data analytics, machine learning is a method used to devise complex prediction algorithms and models [12]. Machine learning models that can deliver fast and accurate results given appropriate data are provided [13].

In this work, we apply machine learning to the 2015 Residential Energy Consumption Survey Data for building occupant characteristics classification and prediction which can lead to automating some steps of building occupant persona development. The procedure includes data processing, feature engineering, selection of target variables, training, and evaluating the models. This work aims to answer the following research questions:

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  RQ1: How effectively can machine learning tools predict individual building occupant characteristics?

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  RQ2: How do machine learning algorithms compare with each other when predicting building occupant characteristics?

The rest of the paper is structured as follows: Section 2 reviews relevant studies conducted in the domain of building occupant persona development and machine learning methods for occupant behavior modeling. Section 3 presents the methodology, description of the data, and machine learning models used. Section 4 demonstrates the machine learning evaluation details. Section 5 discusses the development of building occupant persona using the findings of this work, the limitations, and, the future research directions. Finally, section 6 concludes the research.

## Section snippets

## Occupant behavior and building energy performance

The building occupant persona is an effective and concise way to represent these characteristics for human-centered building design and operation. Several studies have investigated the relationship between occupant behavior and building performance, focusing on different aspects and contexts, such as performance assessment, energy efficiency & policies, consumption discrepancies, building control, and, residential buildings.

Studies have highlighted the need to consider occupant perspectives and

## Methodology

Developing occupant characteristics persona is a difficult and time-consuming task [4]. Using the latest machine learning tools can help alleviate the process. However, there are some challenges to using machine learning tools in the development of building occupant persona. This section describes the challenges and the methodology followed by this work to overcome these challenges in using state-of-the-art machine learning models in occupant characteristics classification and prediction.

## Evaluation

The evaluation of the machine learning models plays a crucial role in assessing the performance of the classification models. Employing appropriate evaluation settings and metrics, analyzing performance scores, and, identifying key findings can draw meaningful conclusions about the model’s effectiveness and its relevance to the research objectives. The evaluation section forms an essential component of the study, contributing to the overall understanding and interpretation of the classification

## Discussion

The classification and prediction of occupant characteristics using machine learning tools can be useful in many ways. It can provide a way of partially automating the building occupant persona generation procedure which can facilitate both the occupants and the building designers in many ways. The following is an example case study of occupant persona generated from the feature predictions from this work followed by a discussion on the shortcomings of this study along with the possible future

## Conclusion

This research investigates the feasibility of using machine learning for the classification of occupant characteristics depending on housing and energy consumption data with a view of automating some steps of building persona development. In this study, six machine learning models have been utilized using the 2015 Residential Energy Consumption Survey data from the U.S. Energy Information Administration (EIA). The results indicate that it is possible to use machine learning tools for the

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

We would like to thank the anonymous reviewers for their valuable comments on the earlier version of the manuscript. This work was supported by NSF-1845446.

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