Residential activity pattern modelling through stochastic chains of variable memory length
Residential activity modelling has attracted considerable attention over the last years. This is particularly due to the fact that residential energy demand loads are highly dependent on the activity patterns of the household. Therefore, activity models are being increasingly used to underpin high-r...
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| Format: | Journal article |
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Elsevier
2019
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| _version_ | 1826301497730662400 |
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| author | Ramírez-Mendiola, J Grünewald, P Eyre, N |
| author_facet | Ramírez-Mendiola, J Grünewald, P Eyre, N |
| author_sort | Ramírez-Mendiola, J |
| collection | OXFORD |
| description | Residential activity modelling has attracted considerable attention over the last years. This is particularly due to the fact that residential energy demand loads are highly dependent on the activity patterns of the household. Therefore, activity models are being increasingly used to underpin high-resolution energy demand models. This paper details the implementation of a new methodology for the analysis of empirical activity data that allows for the identification of characteristic behavioural patterns within them. The identified patterns are then used as the basis for the construction of a high-resolution residential user activity model. The model attempts to capture the statistical characteristics of the empirical data in the form of a stochastic process with memory of variable length. The proposed model is compared to a model based on the predominant first-order Markov chain approach. In addition to the modelling approach, a new metric for assessing the quality of activity sequences simulations is proposed. Given the amount of empirical data contained in any of the individual time-use datasets currently available, it would appear that the performance improvement over the predominant first-order Markov chain approach is modest. However, the validation results show that the proposed approach has the potential for broadening our understanding of the scheduling of activities in people’s day-to-day lives and how this relates to the observed variability in both activity and energy consumption patterns. |
| first_indexed | 2024-03-07T05:33:20Z |
| format | Journal article |
| id | oxford-uuid:e306b46b-0671-45d2-a21d-a424b2975c87 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T05:33:20Z |
| publishDate | 2019 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:e306b46b-0671-45d2-a21d-a424b2975c872022-03-27T10:05:49ZResidential activity pattern modelling through stochastic chains of variable memory lengthJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:e306b46b-0671-45d2-a21d-a424b2975c87Symplectic Elements at OxfordElsevier2019Ramírez-Mendiola, JGrünewald, PEyre, NResidential activity modelling has attracted considerable attention over the last years. This is particularly due to the fact that residential energy demand loads are highly dependent on the activity patterns of the household. Therefore, activity models are being increasingly used to underpin high-resolution energy demand models. This paper details the implementation of a new methodology for the analysis of empirical activity data that allows for the identification of characteristic behavioural patterns within them. The identified patterns are then used as the basis for the construction of a high-resolution residential user activity model. The model attempts to capture the statistical characteristics of the empirical data in the form of a stochastic process with memory of variable length. The proposed model is compared to a model based on the predominant first-order Markov chain approach. In addition to the modelling approach, a new metric for assessing the quality of activity sequences simulations is proposed. Given the amount of empirical data contained in any of the individual time-use datasets currently available, it would appear that the performance improvement over the predominant first-order Markov chain approach is modest. However, the validation results show that the proposed approach has the potential for broadening our understanding of the scheduling of activities in people’s day-to-day lives and how this relates to the observed variability in both activity and energy consumption patterns. |
| spellingShingle | Ramírez-Mendiola, J Grünewald, P Eyre, N Residential activity pattern modelling through stochastic chains of variable memory length |
| title | Residential activity pattern modelling through stochastic chains of variable memory length |
| title_full | Residential activity pattern modelling through stochastic chains of variable memory length |
| title_fullStr | Residential activity pattern modelling through stochastic chains of variable memory length |
| title_full_unstemmed | Residential activity pattern modelling through stochastic chains of variable memory length |
| title_short | Residential activity pattern modelling through stochastic chains of variable memory length |
| title_sort | residential activity pattern modelling through stochastic chains of variable memory length |
| work_keys_str_mv | AT ramirezmendiolaj residentialactivitypatternmodellingthroughstochasticchainsofvariablememorylength AT grunewaldp residentialactivitypatternmodellingthroughstochasticchainsofvariablememorylength AT eyren residentialactivitypatternmodellingthroughstochasticchainsofvariablememorylength |