Introducing a novel method for simulating stochastic movement and occupancy in residential spaces using time-use survey data

Zhang, Ruiming; Zhou, Tongyu; Ye, Hong; Darkwa, Jo

Abstract

In the context of growing concerns over energy consumption and sustainability, accurate modelling of occupancy patterns within residential buildings is critical. In this study, a novel stochastic occupancy model is introduced for simulating human behaviour within residential buildings by employing Time Use Survey (TUS) data and utilising Markov chains and probabilistic sampling algorithms. The novelty of this research lies in its approach to represent the dynamic nature of occupancy across different functional spaces and age groups, a gap not yet adequately addressed in existing studies. The model's accuracy is ascertained through ten-fold cross-validation, achieving an average R2 value of 0.91 across key functional rooms (bedroom, bathroom, kitchen, living room), indicating a high degree of precision. Applied to a case study of a two-story detached house in the UK, the model effectively reflects varied behaviour patterns and room occupancy among different age groups. For instance, the average daily appliance energy consumption for occupants aged 8–14 ranged from 0 to 3.77 kWh (median 1.71 kWh), for ages 15–64 from 0 to 4.93 kWh (median 2.61 kWh), and for over 65 from 0.87 to 5.65 kWh (median 3.60 kWh). This model, with its scalability and accuracy in capturing the inherent randomness of human behaviour, is a valuable tool for improving energy consumption simulations and contributing to sustainable residential building design and management.

Citation

Zhang, R., Zhou, T., Ye, H., & Darkwa, J. (2024). Introducing a novel method for simulating stochastic movement and occupancy in residential spaces using time-use survey data. Energy and Buildings, 304, Article 113854. https://doi.org/10.1016/j.enbuild.2023.113854

• Journal Article Type: Article
• Acceptance Date: Dec 21, 2023
• Online Publication Date: Dec 27, 2023
• Publication Date: Feb 1, 2024
• Deposit Date: Jan 2, 2024
• Publicly Available Date: Dec 28, 2024
• Journal: Energy and Buildings
• Print ISSN: 0378-7788
• Electronic ISSN: 1872-6178
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 304
• Article Number: 113854
• DOI: https://doi.org/10.1016/j.enbuild.2023.113854
• Keywords: Electrical and Electronic Engineering; Mechanical Engineering; Building and Construction; Civil and Structural Engineering
• Public URL: https://nottingham-repository.worktribe.com/output/29262992
• Publisher URL: https://www.sciencedirect.com/science/article/abs/pii/S0378778823010848?via%3Dihub