The impact of occupants' behaviour on building energy demand

Haldi, Frédéric; Robinson, Darren

doi:10.1080/19401493.2011.558213

Cited by 210 publications

(91 citation statements)

References 26 publications

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“…More recently, Vorger (2014) hard coded models of presence, activities, approximate use of heating systems, window and blind interaction within the building simulation software Pleiades+COMFIE. In line with empirical findings, Haldi and Robinson (2011) found with CitySim that variations in stochastic behaviour accounted for a factor of two variation in heating demand. Likewise, when comparing an ideal and worst…”

Section: Building Performance Deviationssupporting

confidence: 74%

“…These range from hard coded integration in the case of lighting behaviour models in Reinhart's (2004) Lightswitch2002 algorithm to Haldi and Robinson's (2011) integration of occupant presence, window and blind models into CitySim. More recently, Vorger (2014) hard coded models of presence, activities, approximate use of heating systems, window and blind interaction within the building simulation software Pleiades+COMFIE.…”

Section: Building Performance Deviationsmentioning

confidence: 99%

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On the multi-agent stochastic simulation of occupants in buildings

Chapman

Siebers

Robinson

2018

Journal of Building Performance Simulation

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One of the principle causes for deviations between predicted and simulated performance of buildings relates to the stochastic nature of their occupants: their presence, activities whilst present, activity dependent behaviours and the consequent implications for their perceived comfort. A growing research community is active in the development and validation of stochastic models addressing these issues; and considerable progress has been made. Specifically models in the areas of presence, activities while present, shading devices, window openings and lighting usage.One key outstanding challenge relates to the integration of these prototype models with building simulation in a coherent and generalizable way; meaning that emerging models can be integrated with a range of building simulation software. This thesis describes our proof of concept platform that integrates stochastic occupancy models within a multi agent simulation platform, which communicates directly with building simulation software. The tool is called Nottingham Multi-Agent Stochastic Simulation (No-MASS).No-MASS is tested with a building performance simulation solver to demonstrate the effectiveness of the integrated stochastic models on a residential building and a non-residential building. To account for diversity between occupants No-MASS makes use of archetypical behaviours within the stochastic models of windows, shades and activities. Thus providing designers with means to evaluate the performance of their designs in response to the range of expected behaviours and to evaluate the robustness of their design solutions; which is not possible using current simplistic deterministic representations. No-MASS employs agent machine learning techniques that allow them to learn how to respond to the processes taking place within a building and agents can choose a strategy without the need for context specific rules.Employing these complementary techniques to support the comprehensive simulation of occupants presence and behaviour, integrated within a single platform that can readily interface with a range of building (and urban) energy simulation programs is the key contribution to knowledge from this thesis. Nevertheless, there is significant scope to extend this work to further reduce the performance gap between simulated and real world buildings.

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Section: Building Performance Deviationssupporting

confidence: 74%

Section: Building Performance Deviationsmentioning

confidence: 99%

On the multi-agent stochastic simulation of occupants in buildings

Chapman

Siebers

Robinson

2018

Journal of Building Performance Simulation

Self Cite

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“…Some studies attributed the building model uncertainty to occupants' behaviors and have developed the models that can explain the presence and behaviors of occupants Page et al 2007;Haldi and Robinson 2011). Findings from these studies suggest that building occupants' behaviors have a significant impact on the building energy performance.…”

Section: Stochastic Building Energy Models For Individual Buildingsmentioning

confidence: 89%

Review on stochastic modeling methods for building stock energy prediction

Lim

Zhai

2017

Build. Simul.

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Increasing risks of energy security and greenhouse gas emission due to the growing urbanization trend have prompted the need for urban energy demand prediction and management, in which the building energy consumption is the main cause. This paper reviews the recent advances and state-of-the-art in modeling building stock energy consumption, including both the top-down and bottom-up approaches. The study compares and summarizes the strengths and weaknesses of each primary method. Specific focus has been paid to the bottom-up stochastic engineering modeling methods, which hold sound quantitative theory bases as well as considering uncertain reality conditions. Stochastic building stock energy models account for the uncertainties that are the main limitation in existing building stock models. Discussions are provided regarding the process in the current stochastic building stock energy model. Challenges and possible future directions are examined for the improvement of stochastic building stock energy model.

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“…Hay que considerar que el incremento en el número de usuarios en una vivienda, no se traduce en un incremento lineal en el consumo de energía (Haldi y Robinson, 2010;Fujimoto, Shimoda, y Yamaguchi, 2011). Por tal razón, los usuarios son incluidos en la estimación de la energía como el logaritmo de su número, garantizando así un incremento en el consumo de energía de esa aplicación (Kema-Xenergy, 2004), (Kema, 2010) (KEMA-XENERGY, 2004; KEMA, Inc., 2010) (Ecuación 2): Ecuación 2 S = Número de aplicaciones por usuario.…”

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Modelos 3D de ciudades como herramienta para la estimación de la demanda energética

Sánchez¹

2015

Ontare

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ResumenEn esta investigación, se presenta una metodología para la estimación de la demanda de energía eléctrica de edificios, empleando modelos de ciudades 3D. La idea de incluir este tipo de información, es que en la actualidad son ricos en datos geométricos y semánticos, los cuales pueden llegar a ser útiles en el pronóstico de demandas de energía. Inicialmente, se presentan los conceptos básicos de sistemas de información geográfica y de energía eléctrica, siguiendo con la selección del método que mejor se ajuste a los requerimientos del proyecto.Posteriormente, se presentan las herramientas de procesamiento para archivos CityGML y luego, con la metodología para la estimación de la demanda de energía eléctrica, finalizando con la presentación de un diagrama de actividades, que expresa de mejor forma las ideas expuestas. Después, se presentan los resultados en un área de estudio empleada durante la implementación y finalmente, las conclusiones. PalabRas claveModelos 3D, energía eléctrica, demanda de energía, sistemas de información geográfica. ______________

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The impact of occupants' behaviour on building energy demand

Cited by 210 publications

References 26 publications

On the multi-agent stochastic simulation of occupants in buildings

On the multi-agent stochastic simulation of occupants in buildings

Review on stochastic modeling methods for building stock energy prediction

Modelos 3D de ciudades como herramienta para la estimación de la demanda energética

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