Now showing 1 - 10 of 11
  • 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
    High Resolution Space - Time Data: Methodology for Residential Building Simulation Modelling
    (International Building Performance Simulation Association (IBPSA), 2013-08-28) ; ; ; ;
    A bottom-up approach is developed for the specification of operational data with a high spacetime resolution, to be used as inputs in multi-zone residential building models. These archetype models will be used to analyse demand modulation of total domestic electricity consumption, thus requiring a detailed knowledge of domestic loads. The approach is based on national Time-Use Survey (TUS) resident activity data. To illustrate the approach, the EnergyPlus simulation platform is used to model a multi-zone case study building. Occupancy profiles, lighting load and disaggregated electrical appliance load profiles, as well as their associated heat gains, are generated and spatially mapped within the building. A good match is seen between synthesised and measured profiles. A greater sharing of electrical appliances, as the household size increases, is also seen. Fifteen-minute resolution of the model outputs is found to be sensible in the context of the current project, due to aggregation.
      344
  • Publication
    Prediction of Residential Building Demand Response Potential Using Data-Driven Techniques
    This paper is concerned with the evaluation of the ability of data-driven predictive models to capture the demand response potential in residential buildings. A mid-floor apartment with an air to water heat pump for space heating, utilised as an archetype dwelling, is simulated using EnergyPlus. The research is focused on forecasting the electrical demand from the heating load for the coldest month of the year, considering two types of DR events, load reduction and load increase. After the generation of the synthetic database, an artificial neural network model and a support vector machine model are examined regarding their ability to predict the electrical demand from heating loads.
      145
  • 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
    Definition of a useful minimal-set of accurately-specified input data for Building Energy Performance Simulation
    Developing BEPS models which predict energy usage to a high degree of accuracy can be extremely time consuming. As a result, assumptions are often made regarding the input data required. Making these assumptions without introducing a significant amount of uncertainty to the model can be difficult, and requires experience. Even so, rules of thumb from one geographic region are not automatically transferrable to other regions. This paper develops a methodology which can be used to determine useful guidelines for defining the most influential input data for an accurate BEPS model. Differential sensitivity analysis is carried out on parametric data gathered from five archetype dwelling models. The sensitivity analysis results are used in order to form a guideline minimum set of accurately defined input data. Although the guidelines formed apply specifically to Irish residential dwellings, the methodology and processes used in defining the guidelines is highly repeatable. The guideline minimum data set was applied to practical examples in order to be validated. Existing buildings were modelled, and only the parameters within the minimum data set are accurately defined. All building models predict annual energy usage to within 10% of actual measured data, with seasonal energy profiles well-matching.
    Scopus© Citations 41  992
  • 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
    Natural ventilation in residential building archetypes: a stochastic approach based on occupant behaviour and thermal comfort
    (International Building Performance Simulation Association (IBPSA), 2014-05-10) ; ; ; ;
    As houses become more energy efficient due to highly thermal resistant fabrics, the impact of natural ventilation on indoor comfort and on transient heating and cooling loads increases. These two constraints must be integrated within building performance simulation models when assessing the potential for electrical load shifting strategies in residential buildings placed in a smart grid environment. A natural ventilation model is developed and implemented for five residential building archetypes. A bottom-up methodology based on occupant behaviour, through the use of time-of-use data, is implemented at room level within EnergyPlus. A stochastic approach determines whether to open or close windows, depending on the occupancy state, the activity type and level, and the thermal comfort experienced. The algorithms proposed consider the main drivers governing window operation within a residential context. Focus is placed on the modelling challenges, and the impacts of the model are assessed using energy performance and thermal comfort.
      253
  • Publication
    Lumped Parameter Building Model Calibration using Particle Swarm Optimization
    This paper presents a methodology for the automated calibration of deterministic lumped parameter models in building energy simulation using optimization methods. A heterogeneous model topology is proposed to represent a residential building archetype developed in the EnergyPlus simulation environment. The archetype model has previously been used to characterize the domestic building stock in Ireland. The automated calibration problem is solved as a least squares error problem solved using a local optimization method (Sequential Quadratic Programming) and two heuristics methods (Particle Swarm Optimization and Genetic Algorithm). It is shown that Particle Swarm Optimization provides the best performance for this particular problem and provides an inherent robustness under model uncertainty.
      633