Influence of Occupant Behavior for Building Energy Conservation: A Systematic Review Study of Diverse Modeling and Simulation Approach

Uddin, Mohammad Nyme; Wei, Hsi Hsien; Chi, Hung-Lin; Ni, Meng

doi:10.3390/buildings11020041

Cited by 39 publications

(22 citation statements)

References 116 publications

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“…In addition, having a larger refrigerator and freezer also increased some households' food consumptionanother possible side-effect not considered when designing for household behavior. Indeed, sometimes modeling household behavior seems to be done without the knowledge of how complex behavior is [1,32]. Perhaps the living labs that are developed at many universities might do well in focusing on detecting unforeseen consequences that can lead to the opposite effect of what was originally intended-especially if it increases rather than decreases GHG emissions.…”

Section: Discussionmentioning

confidence: 99%

My Fault or Default—Household Behaviors Living in a Climate-Smart Building

Wester

2022

Buildings

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Decreasing climate impact of households is a concern for many actors. One way to address this challenge is to focus on household behaviors related to energy use and consumption. Another is to construct climate-smart houses that encourage households to utilize smart functions that reduce emissions. This article examines the link between these two strategies and investigates how living in a climate-smart house affects household behaviors, by conducting interviews with households before and after they moved into a climate-neutral house. Results indicate that overall, emissions decrease after the move, mostly due to the features of the building itself rather than changes in behavior. Unintended effects were also observed, calling into question which strategy is most effective in reducing a household’s climate footprint.

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Section: Discussionmentioning

confidence: 99%

My Fault or Default—Household Behaviors Living in a Climate-Smart Building

Wester

2022

Buildings

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“…Five candidate ML methods have been chosen for inquiry to further explain their performance in ML for both binary and [17] multi-class-occupancy prediction problems. These models are less complex than many of the more recent developments in this field, but they are well known, acting as performance baselines regularly.…”

Section: Candidate Modelmentioning

confidence: 99%

“…Random Forests (RF) are a collection of various decision trees that are [17] applied sequentially from a root (parent) node to a terminal (or child) node to predict the behavior described by trained data [77]. [82] This technique provides several conditional rules that can be as easy as comparing a sensor reading to a threshold to match data samples by related traits.…”

Section: Random Forestmentioning

confidence: 99%

“…Consequently, a non-intrusive technique using indoor environmental (IE) sensing was introduced in [13,8,14,15,16] to measure the level of indoor variables, including temperature, humidity, and light intensity, to deduce the occupancy status and number. Studies in [17,18] demonstrate how occupancy parameters can be used to finetune the energy consumption through the " energy use per person" to establish a stochastic parameter correlation between occupancy and energy usage. The results show 8.9% in the classroom, 3.1% in an office environment, and 1.3% in the computer room energy-saving potential.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of Machine Learning Methods for Non-Intrusive Indoor Occupancy Detection and Estimation

Aliero¹,

Pasha²,

Toosi³

et al. 2021

Preprint

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Occupancy-driven application research has been active research for a decade that focuses on improving or replacing new building infrastructure to improve building energy efficiency. Existing approaches for HVAC energy saving are putting more emphasis on occupancy detection, estimation, and localization to trade-off between energy consumption and thermal comfort satisfaction. In a non-intrusive approach, various sensors, actuators, and analytic data methods are commonly used to process data from occupant surroundings and trigger appropriate action to achieve the task. However, the performance of the non-intrusive approach reported in the literature is relatively poor due to the lack of quality of dataset used in model training and expropriate choice of machine learning model. This study proposed a non-intrusive approach that to improve the collection and quality of dataset using data pre-processing. The study collected a training dataset using various sensors installed in the building and developed a model using five machine learning models to determine occupant’s presence and estimate their number in the building. The proposed solution is tested in the living room with a prototype system integrated with various sensors designed to obtain occupant surrounding environmental datasets. The model’s prediction results obtained indicate that it is possible for the proposed solution to obtain data, process, and predict the occupant number with high accuracy (73.6 -99.7% using random forest).

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“…Yet, these behaviours are difficult to reliably model due to a lack of understanding and supporting data. The third article by Uddin et al (2021) [7] performed a systematic review of relevant behavioural studies of building occupants to summarise the current state of the topic. Their summary of 83 articles published in the past decade compared the different modelling approaches and the influence of certain parameters on occupant energy conservation behaviour.…”

mentioning

confidence: 99%