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Buttitta, Giuseppina
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Buttitta, Giuseppina
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Buttitta, Giuseppina
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Now showing 1 - 4 of 4
- PublicationModelling residential building stock heating load demand - Comparison of occupancy models at large scale(2017-06-28)
; ; ; In the residential housing sector, a strong correlation exists between occupant behaviour and space heating energy use. In particular, the occupancy scenario (e.g., daytime absence, morning presence, etc.) has a significant influence on residential heating load profiles, as well as on cumulative heating energy consumption. In the literature, many occupancy models have been utilised to predict occupancy profiles of individual dwellings as part of larger residential building stocks. The choice of the most suitable occupancy model is a trade-off between complexity, accuracy and computational effort, as well as model integration at large scale. The current paper analyzes the combined influence of different occupancy assumptions and different occupancy models on housing heating loads for a UK building stock sample. The building stock heating loads are estimated using a dynamic thermal model based on an equivalent Resistance-Capacitance electric circuit. It is assumed that the heating periods are coincident with the actively occupied periods. The actively occupied periods are first determined using two existing consolidated occupancy models, and then by using newly developed probabilistic occupancy models. All the models are characterised by a different grade of complexity and accuracy. Comparing the results of all the presented methodologies, the advantages of the new probabilistic approaches are analyzed.238 - PublicationModelling Household Occupancy Profiles using Data Mining Clustering Techniques on Time Use Data(2017-08-09)
; ; ; A strong correlation exists between occupant behaviour and energy demand in residential buildings. The choice of the most suitable occupancy model to be integrated in high temporal resolution energy demand simulations is heavily in uenced by the purpose of the building energy demand model and it is a tradeoff between complexity and accuracy. The current paper introduces a new occupancy model that produces multi-day occupancy profiles and can be adaptable to various occupancy scenarios (e.g., at home all day, mostly absent) and scalable to different population sizes. The methodology exploits data mining clustering techniques with Time Use Survey (TUS) data to produce realistic building occupancy patterns. The overall methodology can be subdivided into two steps: 1. Identification and grouping of households with similar daily occupancy profiles, using data mining clustering techniques; 2. Creation of probabilistic occupancy profiles using 'inverse function method'. The data from the model can be used as input to residential dwelling energy models that use occupancy time-series as inputs.344Scopus© Citations 5 - PublicationClustering of household occupancy profiles for archetype building modelsThe continued penetration of renewable energy sources in electricity generation and the de-carbonization of the domestic space heating and hot water sectors is increasing the importance of demand side management (DSM). The development of end-use energy consumption models that can be easily integrated with electricity dispatch models is crucial for the assessment of the integration of supply and demand. The energy consumption of the domestic building stock is highly correlated with occupant behaviour, however the inclusion of occupant behaviour in energy models is challenging due to its highly variable nature. Nevertheless, in order to obtain reliable models of domestic energy consumption at high time resolution, the analysis of occupant behaviour patterns is fundamental. This paper aims to develop a new methodology to generate realistic occupancy patterns that can be representative of large numbers of households. This method is based on the clustering of household occupancy profiles using the UK 2000 Time Use Survey data as a case study. The occupancy profiles that result from this method can be used as input to residential building energy end-use models, thereby giving improved overall model performance.
713Scopus© Citations 40 - PublicationDevelopment of occupancy-integrated archetypes: Use of data mining clustering techniques to embed occupant behaviour profiles in archetypesBuilding stock modelling usually deploys representative building archetypes to obtain reliable results of annual energy heating demand and to minimise the associated computational cost. Available methodologies define archetypes considering only the physical characteristics of buildings. Uniform occupancy schedules, which correspond to national averages, are generally used in archetype energy simulations, despite evidence of occupancy schedules which can vary considerably for each building. This paper presents a new methodology to define occupancy-integrated archetypes. The novel feature of these archetype models is the integration of different occupancy schedules within the archetype itself. This allows building stock energy simulations of national population subgroups characterised by specific occupancy profiles to be undertaken. The importance of including occupant-related data in residential archetypes, which is different than the national average, is demonstrated by applying the methodology to the UK national building stock. The resultant occupancy-integrated archetypes are then modelled to obtain the annual final heating energy demand. It is shown that the relative difference between the heating demand of occupancy-integrated archetypes and uniform occupancy archetypes can be up to 30%.
520Scopus© Citations 33