Linking Building Energy-Load Variations with Occupants’ Energy-Use Behaviors in Commercial Buildings: Non-Intrusive Occupant Load Monitoring (NIOLM)

Rafsanjani, Hamed Nabizadeh

doi:10.1016/j.proeng.2016.04.041

Cited by 41 publications

(25 citation statements)

References 12 publications

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“…In the literature, various approaches have been proposed to investigate the effect of occupant behavior on energy consumption in residential buildings. An algorithm was presented by [55] to simulate any occupancy pattern of any building type based on defined inputs. The model uses data to generate arrival, presence, and departure times that could affect residential energy consumption.…”

Section: Shared Systemsmentioning

confidence: 99%

Shared Solar and Battery Storage Configuration Effectiveness for Reducing the Grid Reliance of Apartment Complexes

2020

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More than 2 million houses in Australia have installed solar photovoltaic (PV) systems; however, apartment buildings have adopted a low percentage of solar PV and battery storage installations. Given that grid usage reduction through PV and battery storage is a primary objective in most residential buildings, apartments have not yet fully benefited from installations of such systems. This research presents shared microgrid configurations for three apartment buildings with PV and battery storage and evaluates the reduction in grid electricity usage by analyzing self-sufficiency. The results reveal that the three studied sites at White Gum Valley achieved an overall self-sufficiency of more than 60%. Owing to the infancy of the shared solar and battery storage market for apartment complexes and lack of available data, this study fills the research gap by presenting preliminary quantitative findings from implementation in apartment buildings.

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Section: Shared Systemsmentioning

confidence: 99%

Shared Solar and Battery Storage Configuration Effectiveness for Reducing the Grid Reliance of Apartment Complexes

2020

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“…Energy savings from optimally selecting HVAC temperatures in office buildings can be as high as 37% [14,15], although the savings differ based on the climate, building materials, and size. In addition, occupant behavior and utilization of the new technologies requires in buildings further explorations [48][49][50][51] . It is worth mentioning that the trade-off between the complexity of the sensing devices and controllers and the energy savings can play an important role for the level of user adaptability of these methods [49,[52][53][54][55][56][57], which requires further investigations.…”

Section: Discussionmentioning

confidence: 99%

Towards unsupervised learning of thermal comfort using infrared thermography

Ghahramani¹,

Castro²,

Karvigh³

et al. 2018

Applied Energy

138

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Maintaining thermal comfort in built environments is important for occupant health, well-being, and productivity, and also for efficient HVAC system operations. Most of the existing personal thermal comfort learning methods require occupants to provide feedback via a survey to label the monitored environmental or physiological conditions in order to train the prediction models. However, the accuracy of these models usually drops after the training process as personal thermal comfort is dynamic and changes over time due to climatic variations and/or acclimation. In this paper, we present a hidden Markov model (HMM) based learning method to capture personal thermal comfort using infrared thermography of human face. We chose human face since its blood vessels has a higher density and it is not covered while performing regular activities in built environments. Learning algorithm has 3 hidden states (i.e., uncomfortably warm, comfortable, uncomfortably cool) and uses discretization for forming the observed states from the continuous infrared measurements. The approach can potentially be used for continuous monitoring of thermal comfort to capture the variations over time. We tested and validated the method in a fourday long experiment with 10 subjects and demonstrated an accuracy of 82.8% for predicting uncomfortable conditions.

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“…In [46], authors describe the Non-Intrusive Occupant Load Monitoring (NIOLM) framework, which evaluates WiFi connection/disconnection events within a commercial building to estimate starting and ending of individual occupants' energy-consuming behaviours.…”

Section: Smartphonesmentioning

confidence: 99%

Occupant Activity Detection in Smart Buildings: A Review

Cardinale

2020

SSRN Journal

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Building management systems (BMS) in smart buildings are supposed to support the optimization of energy and resources consumption, while ensuring basic users' comfort. A common and effective optimizing strategy is to detect, with high accuracy, room occupancies, events, and activities that occur within a building, to accordingly control the energy usage. Several approaches have been implemented to achieve this goal, combining many technologies (e.g., sensor networks, machine learning techniques) as well as new data sources (e.g., sensed data, social networks) allowing to better detect occupant activities. In this context, the purpose of this study is twofold: (i) identify existing solutions related to capturing occupant activities and events to better manage energy usage and provide occupants' comfort, and (ii) pin down the lessons to learn from existing approaches and technologies in order to design better solutions in this regard. We do not pretend to give an exhaustive revision, but throughout this review, we aim at showing that several data can significantly enrich the typology and content of information managed to detect occupant activities and highlight new possibilities in terms of activities diagnosis and analysis to generate more opportunities in optimizing the energy consumption and providing comfort in smart building.

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Linking Building Energy-Load Variations with Occupants’ Energy-Use Behaviors in Commercial Buildings: Non-Intrusive Occupant Load Monitoring (NIOLM)

Cited by 41 publications

References 12 publications

Shared Solar and Battery Storage Configuration Effectiveness for Reducing the Grid Reliance of Apartment Complexes

Shared Solar and Battery Storage Configuration Effectiveness for Reducing the Grid Reliance of Apartment Complexes

Towards unsupervised learning of thermal comfort using infrared thermography

Occupant Activity Detection in Smart Buildings: A Review

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