Now showing 1 - 8 of 8
  • Publication
    A high-temporal resolution residential building occupancy model to generate high-temporal resolution heating load profiles of occupancy-integrated archetypes
    (Elsevier, 2020-01-01) ;
    A strong correlation exists between occupant behaviour and space heating energy use. In particular, the occupancy status (e.g., daytime absence) is known to have 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 the larger residential building stock. However, none of the existing models consider diversity associated with occupancy-integrated archetypes to generate high-temporal resolution heating load profiles. The current paper uses Time Use Survey (TUS) data to develop a high-temporal resolution residential building occupancy model. The key feature of the proposed model, implemented using MATLAB, is the ability to generate stochastic occupancy time-series data for national population subgroups characterised by specific occupancy profiles. It is shown that the results are capable of closely approximating data available from TUS. The developed model can be applied to improve the quality of modelled high-temporal resolution heating load profiles for generic building stock characterised by population subgroups represented by different occupancy-integrated archetypes. A case study is performed on a building stock sample located in London, UK. The developed occupancy model has been implemented in MATLAB and is available for download.
    Scopus© Citations 24  426
  • Publication
    Utilising time of use surveys to predict domestic hot water consumption and heat demand profiles of residential building stocks
    (SCIENCEDOMAIN International, 2016-06) ; ; ;
    Aims: The prediction of water consumption patterns is a challenge, especially when water metering is not available at scale. The use of time-of-use survey (TUS) data offers an alternative to metering in order to track the general patterns of water consumption across large and representative groups of end-users. The paper focuses on the prediction of analytical domestic hot water (DHW) demand profiles for detailed building archetype models, using an occupant focused approach based on TUS data. The paper illustrates and discusses the resulting capability of dwelling archetypes to capture variations in heat demand and energy usage for water heating on a national scale and at high time resolution. Methodology: Five dwelling types are considered over different construction periods, representative of the majority of the Irish residential stock, which is used here as a case study. They are modelled at room level using EnergyPlus and converted into archetype models. A bottom-up approach is utilised to develop the required operational data at high space and time resolution. That methodology applies Markov Chain Monte Carlo techniques to TUS activity data to develop activity-specific profiles for occupancy and domestic equipment electricity use. It is extended to DHW demand profiles by combining the probability distributions for particular TUS activities with average daily DHW consumptions, depending on the household size, day type and season. Results: The archetype models capture variations in DHW consumption, heat demand and energy usage for DHW heating, on a national scale and a fifteen-minute basis. Moreover, they are found to be 90% accurate with the Irish standard dwelling energy assessment procedure in estimating the annual energy requirements for DHW heating. Conclusion: This study demonstrates the potential for utilising time of use surveys to predict domestic water demand profiles on a national scale and at high time resolution.
      1010
  • Publication
    Modelling residential building stock heating load demand - Comparison of occupancy models at large scale
    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.
      237
  • Publication
    Quantification and characterization of energy flexibility in the residential building sector
    (International Building Performance Association, 2019-09-04) ; ; ; ; ;
    Demand response can enable residential consumers to take advantage of control signals and/or financial incentives to adjust the use of their resources at strategic times. These resources usually refer to energy consumption, locally distributed electricity generation, and energy storage. The building structural mass has an inherent potential either to modify consumption or to be used as a storage medium. In this paper, the energy flexibility potential of a residential building thermal mass for the winter design day is investigated. Various active demand response strategies are assessed using two flexibility indicators: the storage efficiency and storage capacity. Using simulation, it is shown that the available capacity and efficiency associated with active demand response actions depend on thermostat setpoint modulation, demand response event duration, heating system rated power and current consumption.
      250
  • Publication
    Developing building archetypes for electrical load shifting assessment: Analysis of Irish residential stock
    Appropriate use of demand side management (DSM) strategies in residential buildings, when placed in a smart grid environment, can help reduce power supplydemand mismatches by shifting electrical loads, thus leading to better integration of renewable energy sources, particularly wind and solar generation. In the current paper, detailed building energy simulation models of residential stock are developed, using an occupant focused approach. Five archetypes are considered over three construction periods, representative of about 82% of the Irish building stock. The archetype models were found to be accurate to within 10% of the Irish standards, as exemplified using the Dwelling Energy Assessment Procedure (DEAP), for space and water heating energy requirements. The proposed approach was found to be more accurate than DEAP to estimate the electricity consumption. By integrating high resolution models for occupancy and electrical equipment use, it can generate more accurate models of the housing stock and expands previous investigations to include occupant behaviour, electrical load shifting and thermal comfort issues.
      593
  • Publication
    Modelling Household Occupancy Profiles using Data Mining Clustering Techniques on Time Use Data
    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.
    Scopus© Citations 5  341
  • Publication
    Utilising time of use surveys to predict water demand profiles of residential building stocks: Irish case study for domestic hot water
    (The WATEF Network, University of Brighton, 2014-09-11) ; ; ;
    The prediction of water consumption patterns is a challenge, especially when water metering is not available at scale. The paper focuses on the prediction of analytical domestic hot water (DHW) demand profiles for detailed building archetype models, using an occupant focused approach based on time-of-use survey (TUS) data. Five dwelling types are considered over different construction periods, representative of the majority of the Irish residential stock, which is used here as a case study. They are modelled at room level using EnergyPlus and converted into archetype models. A bottom-up approach is utilised to develop the required operational data at high space and time resolution. That methodology applies Markov Chain Monte Carlo techniques to TUS activity data to develop activity-specific profiles for occupancy and domestic equipment electricity use. It is extended to DHW demand profiles by combining the probability distributions for particular TUS activities with average daily DHW consumptions, depending on the household size, day type and season. The archetype models are found to be 90% accurate with the Irish standard dwelling energy assessment procedure in estimating the annual energy requirements for DHW heating. Moreover, they capture variations in DHW consumption, heat demand and energy usage for DHW heating, on a national scale and a fifteen-minute basis.
      269
  • Publication
    Development of occupancy-integrated archetypes: Use of data mining clustering techniques to embed occupant behaviour profiles in archetypes
    Building 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%.
    Scopus© Citations 32  517