OccuSpace: Towards a Robust Occupancy Prediction System for Activity Based Workplace

Rahaman, Mohammad Saiedur; Pare, Harsh; Liono, Jonathan; Salim, Flora D.; Ren, Yongli; Chan, Jeffrey; Kudo, Shaw; Rawling, Tim; Sinickas, Alex

doi:10.1109/percomw.2019.8730762

Cited by 16 publications

(16 citation statements)

References 13 publications

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“…In the literature, there are only a few instances of training data using occupants counts [52]. Previous work has shown that the way occupants use buildings is different than expected [53,54] and data from Wifi signals of occupants [55,56], Bluetooth localization [57,58,59], and even text mining [60] can capture signals that could be used as inputs in the modeling process. This type of modeling approach may also influence occupant-centric controls [61].…”

Section: Using Occupant-centric Operations Data In Building Energy Pr...mentioning

confidence: 99%

Limitations of machine learning for building energy prediction: ASHRAE Great Energy Predictor III Kaggle competition error analysis

Miller,

Picchetti,

et al. 2021

Preprint

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Machine learning for building energy prediction has exploded in popularity in recent years, yet understanding its limitations and potential for improvement are lacking. The ASHRAE Great Energy Predictor III (GEPIII) Kaggle competition was the largest building energy meter machine learning competition ever held with 4,370 participants who submitted 39,403 predictions. The test data set included two years of hourly electricity, hot water, chilled water, and steam readings from 2,380 meters in 1,448 buildings at 16 locations. This paper analyzes the various sources and types of residual model error from an aggregation of the competition's top 50 solutions. This analysis reveals the limitations for machine learning using the standard model inputs of historical meter, weather, and basic building metadata. The types of error are classified according to the amount of time errors occur in each instance, abrupt versus gradual behavior, the magnitude of error, and whether the error existed on single buildings or several buildings at once from a single location. The results show machine learning models have errors within a range of acceptability (RMSLE scaled =< 0.1) on 79.1% of the test data. Lower magnitude model errors (0.1 < RMSLE scaled =< 0.3) occur in 16.1% of the test data. These discrepancies can likely be addressed through additional training data sources or innovations in machine learning. Higher magnitude errors (RMSLE scaled > 0.3) occur in 4.8% of the test data and are unlikely to be accurately predicted regardless of innovation. There is a diversity of error behavior depending on the energy meter type (electricity prediction models have unacceptable error in under 10% of test data, while hot water is over 60%) and building use type (public service less than 14%, while technology/science is just over 46%). Most of the instances of continuous error last longer than three days, and significant portions come from individual buildings and collections of buildings from the same site. This analysis forms the foundation for suggestions to reduce machine learning errors by collecting and using additional training data from onsite and web-based sources to improve the capability, accuracy, scalability, and usability of machine learning. Improvement of these metrics could enhance greater adoption in built environment applications.

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Section: Using Occupant-centric Operations Data In Building Energy Pr...mentioning

confidence: 99%

Limitations of machine learning for building energy prediction: ASHRAE Great Energy Predictor III Kaggle competition error analysis

Miller,

Picchetti,

et al. 2021

Preprint

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“…Occupancy data has been used to study short term occupancy in commercial and residential buildings [176,177,178,179]. Space utilisation prediction has been investigated leveraging historical and current contextual data (i.e.…”

Section: Occupant Behaviour Modelingmentioning

confidence: 99%

Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey

Salim

Dong

Ouf

et al. 2020

Building and Environment

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The proliferation of urban sensing, IoT, and big data in cities provides unprecedented opportunities for a deeper understanding of occupant behaviour and energy usage patterns at the urban scale. This enables data-driven building and energy models to capture the urban dynamics, specifically the intrinsic occupant and energy use behavioural profiles that are not usually considered in traditional models. Although there are related reviews, none investigated urban data for use in modelling occupant behaviour and energy use at multiple scales, from buildings to neighbourhood to city. This survey paper aims to fill this gap by providing a critical summary and analysis of the works reported in the literature. We present the different sources of occupant-centric urban data that are useful for data-driven modelling and categorise the range of applications and recent data-driven modelling techniques for urban behaviour and energy modelling, along with the traditional stochastic and simulation-based approaches. Finally, we present a set of recommendations for future directions in data-driven modelling of occupant behaviour and energy in buildings at the urban scale.

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“…These five instances of schemas/ontologies offer -to various extents -features that may be of relevance toward the representation of occupancycentric information. Other examples of building-related ontologies/schemas include BuildingSync 8 , Project Haystack 9 and the Building Topology Ontology 10 . BuildingSync is a schema that focuses on buildings' energy audit data.…”

Section: Metadata Schemasmentioning

confidence: 99%

Current practices and infrastructure for open data based research on occupant-centric design and operation of buildings

Kjærgaard

Ardakanian

Carlucci

et al. 2020

Building and Environment

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OccuSpace: Towards a Robust Occupancy Prediction System for Activity Based Workplace

Cited by 16 publications

References 13 publications

Limitations of machine learning for building energy prediction: ASHRAE Great Energy Predictor III Kaggle competition error analysis

Limitations of machine learning for building energy prediction: ASHRAE Great Energy Predictor III Kaggle competition error analysis

Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey

Current practices and infrastructure for open data based research on occupant-centric design and operation of buildings

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