Home // International Journal On Advances in Intelligent Systems, volume 16, numbers 3 and 4, 2023 // View article

Development of Household Members’ Behavior Model in Urban Scale Using Dynamic Time Warping and Particle Swarm Optimization Algorithms Based on National Time Use Survey

Authors:
Hengxuan Wang
Daisuke Sumiyoshi

Keywords: occupants’ behavior model; household members; public stochastic data; particle swarm optimization; dynamic time warping.

Abstract:
For the target of energy demand estimation of residential buildings in urban scale, occupants’ behavior model has been paid much attention. In this paper, a new model for simulating occupants’ behavior schedules in urban scale has been proposed using only public stochastic data (national lifetime survey) combined with Dynamic Time Warping and Particle Swarm Optimization algorithms. We use this proposed model to generate the 5 type occupant behavior schedules with 5-minutes interval in working and resting day. The generated results - percentages of occupants adopt the given behavior at specific moments are calculated and compared with public stochastic data to verify the accuracy. Also, based on the mutual influence of household members, the generated schedules are filtered and combined into a group of behavior schedules, which is suitable for a household. By the proposed model in this paper, behavior schedule combinations of family members are generated and prepared for residential energy demand calculations in urban scale.

Pages: 63 to 73

Copyright: Copyright (c) to authors, 2023. Used with permission.

Publication date: December 30, 2023

Published in: journal

ISSN: 1942-2679