Data-driven occupant actions prediction to achieve an intelligent building

Pereira, Pedro F.; Ramos, Nuno M. M.; Simões, M. Lurdes

doi:10.1080/09613218.2019.1692648

Cited by 18 publications

(17 citation statements)

References 67 publications

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“…[60]. New ways of monitoring and data analysis have already provided real-time feedback as shown in different spaces like workplaces [46,61,62]. This paper reflects how residential spaces like coliving can undergo a similar transformation by incorporating in-built technology infrastructure POE data analysis to assess use of space.…”

Section: State Of the Art Data-driven Design Through Post-occupancy Evaluation (Poe)mentioning

confidence: 99%

“…Within this changing paradigm, a need for more advanced, digital methods to understand the use of space and improve its design has risen [45,46]; POE and other methods [47,48] The rise in searches for the term 'coliving' in 2015 was likely due to an increase of these facilities in New York City and companies beginning to expand this typology outside of the tech world [33]. Spain is now the country with the fourth highest interest globally, according to Google, and the first in Europe.…”

Section: State Of the Art Data-driven Design Through Post-occupancy Evaluation (Poe)mentioning

confidence: 99%

“…Post-Occupancy Evaluation (POE) is the methodology of obtaining feedback on the use of spaces in a building and its performance for the users [43,44]. Within this changing paradigm, a need for more advanced, digital methods to understand the use of space and improve its design has risen [45,46]; POE and other methods [47,48] are becoming essential to analyzing the current use of spaces, predict performance and ensure housing resiliency [17,[49][50][51][52][53]. Recent studies have shown how POE could improve electricity performance predicted during the design of non-residential spaces [54]; a similar method has been applied for coliving residential spaces.…”

Section: State Of the Art Data-driven Design Through Post-occupancy Evaluation (Poe)mentioning

confidence: 99%

“…[60]. New ways of monitoring and data analysis have already provided real-time feedback, as shown in different spaces like workplaces [46,61]. This paper examined how residential spaces are undergoing a similar transformation, with a special focus on managed solutions like coliving.…”

Section: State Of the Art Data-driven Design Through Post-occupancy Evaluation (Poe)mentioning

confidence: 99%

See 3 more Smart Citations

Data-Driven Methodology for Coliving Spaces and Space Profiling Based on Post-Occupancy Evaluation through Digital Trail of Users

Regodón

Armand²,

Lastres³

et al. 2021

Sustainability

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Sustainable spaces are those that are optimized, accessible, promote user experience and aim to reduce CO2 emissions while enhancing users’ well-being and comfort. The purpose of this paper is to present a methodology that was developed during the COVID-19 pandemic to understand and improve the use of coliving spaces based on remote Post-Occupancy Evaluation (POE) analysis of the digital trail generated by the users. Applying the POE methodology based on data collection from IT infrastructure enabled to identify opportunities to improve the future design of human-centered spaces. The residential market, design-wise traditional for centuries, is now facing a high-speed adaptation to the changing needs, accelerated by the COVID-19 crisis. New ways of living and shared spaces like Coliving are escalating. Technology is both an enabler of this shift in housing and the solution to operating and managing these new buildings. This paper demonstrates, through the case study of a Coliving space located in Madrid, Spain, the benefits of implementing data analysis of the digital trail collected from in-built IT systems such as smart locks, Wi-Fi networks and electric consumption devices. The conclusion is that analysing the available data from the digital infrastructure of coliving buildings can enable practitioners to improve the future design of residential spaces.

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Section: State Of the Art Data-driven Design Through Post-occupancy Evaluation (Poe)mentioning

confidence: 99%

Section: State Of the Art Data-driven Design Through Post-occupancy Evaluation (Poe)mentioning

confidence: 99%

Section: State Of the Art Data-driven Design Through Post-occupancy Evaluation (Poe)mentioning

confidence: 99%

Section: State Of the Art Data-driven Design Through Post-occupancy Evaluation (Poe)mentioning

confidence: 99%

See 2 more Smart Citations

Data-Driven Methodology for Coliving Spaces and Space Profiling Based on Post-Occupancy Evaluation through Digital Trail of Users

Regodón

Armand²,

Lastres³

et al. 2021

Sustainability

View full text Add to dashboard Cite

show abstract

“…Models that use the Markov chain [48][49][50][51][52] or artificial neural networks (ANNs) [45,47,49,50,53] are also common. Similarly, different variants of regression models [20,29,[32][33][34]39,40,[54][55][56][57][58][59][60][61][62] and combinations of regression models that incorporate concepts such as the Theory of Planned Behaviour [37,38] and other classification models including reinforcement learning (RL) [63][64][65] have typically been used for modelling energy-related behaviour. In addition, Markov models have also been used in multiple studies on energy-related behaviour.…”

Section: Types Of Modelling Techniquesmentioning

confidence: 99%

A Meta-Analysis Review of Occupant Behaviour Models for Assessing Demand-Side Energy Consumption

2022

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Occupant behaviour plays a significant role in shaping the dynamics of energy consumption in buildings, but the complex nature of occupant behaviour has hindered a deeper understanding of its influence. A meta-analysis was conducted on 65 published studies that used data-driven quantitative assessments to assess energy-related occupant behaviour using the Knowledge Discovery and Data Mining (KDD) framework. Hierarchical clustering was utilised to categorise different modelling techniques based on the intended outcomes of the model and the types of parameters used in various models. This study will assist researchers in selecting the most appropriate parameters and methods under various data constraints and research questions. The research revealed two distinct model categories being used to study occupant behaviour-driven energy consumption, namely (i) occupancy status models and (ii) energy-related behaviour models. Multiple studies have identified limitations on data collection and privacy concerns as constraints of modelling occupant behaviour in residential buildings. The “regression model” and its variants were found to be the preferred model types for research that models “energy-related behaviour”, and “classification models” were found to be preferable for modelling “occupancy” status. There were only limited instances of data-driven studies that modelled occupant behaviour in low-income households, and there is a need to generate region-specific models to accurately model energy-related behaviour.

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Comparative analysis of window operating behavior in three different open-plan offices in Nanjing

Ren

Zhou

et al. 2021

Energy and Built Environment

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Data-driven occupant actions prediction to achieve an intelligent building

Cited by 18 publications

References 67 publications

Data-Driven Methodology for Coliving Spaces and Space Profiling Based on Post-Occupancy Evaluation through Digital Trail of Users

Data-Driven Methodology for Coliving Spaces and Space Profiling Based on Post-Occupancy Evaluation through Digital Trail of Users

A Meta-Analysis Review of Occupant Behaviour Models for Assessing Demand-Side Energy Consumption

Comparative analysis of window operating behavior in three different open-plan offices in Nanjing

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