Now showing 1 - 10 of 13
  • 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%.
      550Scopus© Citations 35
  • 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.
      363Scopus© Citations 5
  • 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.
  • Publication
    A study of operation strategy of small scale heat storage devices in residential distribution feeders
    Passive operation of thermal energy storage devices is a well established concept in Europe; this paper looks at active operation of thermal storage devices and their role in providing demand response from residential distribution feeders. It co-simulates the power system and the thermal performance of buildings to investigate the effect of operation strategy of thermal energy storage devices on the network and thermal comfort of households. A realistic residential feeder is used to demonstrate the applicability of the presented methodology. It is shown that the operation strategy of the thermal storage devices can affect the realizable reserve from these devices, house temperature and network variables such as losses and voltage. The realizable demand response found by the presented methodology can be used for market operation to avoid underestimation and overestimation of the demand response.
      438Scopus© Citations 6
  • 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.
  • 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.
  • 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.
  • Publication
    An integrated Building-to-Grid model for evaluation of energy arbitrage value of Thermal Storage
    Thermal Electric Storage (TES) has emerged as a promising technology for enhancing the flexibility of the built environment to participate in active Demand Side Management (DSM). These devices allow the decoupling of intra-day scheduling of electric power demand from the time of thermal energy end-use. Therefore, if enabled with communication with the grid, these devices can facilitate load shifting and energy arbitrage. This study evaluates the energy arbitrage value of smart TES devices in residential buildings across Ireland. A Building-to-Grid (B2G) model has been developed which integrates the buildings thermal dynamics and end-use constraints with the power systems economic dispatch model. The thermal behavior of the houses and the TES space heater and hot water tank is modeled through linear state space models for three different mid-flat archetypes. The optimization results show the load shifting and arbitrage potential of TES and its impacts on wind curtailment considering various penetration levels of these devices.
  • 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.
  • 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.