A method for the identification and modelling of realistic domestic occupancy sequences for building energy demand simulations and peer comparison

Aerts, Diederik; Minnen, Joeri; Glorieux, Ignace; Wouters, Ine; Descamps, Filip

doi:10.1016/j.buildenv.2014.01.021

Cited by 156 publications

(83 citation statements)

References 23 publications

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“…However, the analysis presented here and by others ( [2], [18]) has clearly demonstrated that there are broad occupancy patterns related to identifiable household types, and that existing models have underused the available data at this level of differentiation.…”

Section: Model Applications and Limitationsmentioning

confidence: 98%

“…where,P Occupancy Profile Similarity Metric -the process used is generally known as the Levenshtein Edit Distance Method (LEDM) for character string similarity analysis, which is used to compare individual occupancy profiles and is similar to the method used by [2]. The derived metric is hereafter referred to as ProfSim.…”

Section: Verification Mechanismsmentioning

confidence: 99%

“…Aerts et al [2], using a modelling method similar to Wilke [18], differentiated TUS populations by seven common occupancy patterns (e.g. home all day, out from 9am-6pm, etc.)…”

Section: Differentiated Occupant Modelsmentioning

confidence: 99%

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An occupant-differentiated, higher-order Markov Chain method for prediction of domestic occupancy

Flett

Kelly

2016

Energy and Buildings

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This version is available at https://strathprints.strath.ac.uk/56307/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the AbstractHousehold energy demand is closely correlated with occupant and household types and their associated occupancy patterns. Existing occupancy model performance has been limited by a lack of occupant differentiation, poor occupancy duration estimation, and ignoring typical occupancy interactions between related individuals. A Markov-Chain based method for generating realistic occupancy profiles has been developed that aims to improve accuracy in each of these areas to provide a foundation for future energy demand modelling and to allow the occupancy-driven impact to be determined. Transition probability data has been compiled for multiple occupant, household, and day types from UK Time-Use Survey data to account for typical behavioural differences. A higher-order method incorporating ranges of occupancy state durations has been used to improve duration prediction. Typical occupant interactions have been captured by combining couples and parents as single entities and linking parent and child occupancy directly. Significant improvement in occupancy prediction is shown for the differentiated occupant and occupant interaction methods. The higher-order Markov method is shown to perform better than an equivalent higher-order 'event'-based approach. The benefit of the higher-order method compared to a first-order Markov model is less significant and would benefit from more comprehensive occupancy data for an objective comparison.

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Section: Model Applications and Limitationsmentioning

confidence: 98%

Section: Verification Mechanismsmentioning

confidence: 99%

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An occupant-differentiated, higher-order Markov Chain method for prediction of domestic occupancy

Flett

Kelly

2016

Energy and Buildings

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“…For example, some purely activity-based models do not simulate actual household energy demand (Aerts, Minnen, Glorieux, Wouters, & Descamps, 2014;López-Rodríguez, Santiago, Trillo-Montero, Torriti, & Moreno-Munoz, 2013;Wilke, Haldi, Scartezzini, & Robinson, 2013), while Paatero and Lund (2006) simulate appliance use based on deriving switch-on probabilities from monitored electricity consumption data, rather than activity information.…”

Section: The Need For Improved Tools To Evaluate Demand Responsementioning

confidence: 99%

“…Recognising the limitations above, there is a move towards more complex models of activity (Aerts et al, 2014;Flett & Kelly, 2016;Wilke et al, 2013). These are characterised by i) the use of higher-order Markovchains, or 'survival models' of activities, and ii) by providing greater detail about the characteristics of the household occupants and their occupancy patterns.…”

Section: Interconnected Activities and Appliancesmentioning

confidence: 99%

Simulating residential demand response: Improving socio-technical assumptions in activity-based models of energy demand

et al. 2017

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Demand response is receiving increasing interest as a new form of flexibility within low-carbon power systems. Energy models are an important tool to assess the potential capability of demand side contributions. This paper critically reviews the assumptions in current models and introduces a new conceptual framework to better facilitate such an assessment. We propose three dimensions along which change could occur, namely technology, activities and service expectations. Using this framework, the socio-technical assumptions underpinning 'bottom-up' activity-based energy demand models are identified and a number of shortcomings are discussed. First, links between appliance usage and activities are not evidence-based. We propose new data collection approaches to address this gap. Second, aside from thermal comfort, service expectations, which can be an important source of flexibility, are underrepresented and their inclusion into demand models would improve their predicative power in this area. Finally, flexibility can be present over a range of time scales, from immediate responses, to longer term trends. Longitudinal time use data from participants in demand response schemes may be able to illuminate these. The recommendations of this paper seek to enhance the current state-of-the-art in activity-based models and to provide useful tools for the assessment of demand response.

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Activity-Based Modeling for Integration of Energy Systems for House and Electric Vehicle

Yamaguchi

Prakash

Simoda

2020

Power Electronics and Power Systems

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A method for the identification and modelling of realistic domestic occupancy sequences for building energy demand simulations and peer comparison

Cited by 156 publications

References 23 publications

An occupant-differentiated, higher-order Markov Chain method for prediction of domestic occupancy

An occupant-differentiated, higher-order Markov Chain method for prediction of domestic occupancy

Simulating residential demand response: Improving socio-technical assumptions in activity-based models of energy demand

Activity-Based Modeling for Integration of Energy Systems for House and Electric Vehicle

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