Now showing 1 - 5 of 5
  • Publication
    Control strategies for building energy systems to unlock demand side flexibility – A review
    Conventional key performance indicators (KPI) assessed in building simulation lack specific measures of how the building interacts with the grid and its energy flexibility. This paper aims to provide an overview of specific energy flexibility performance indicators, together with supporting control strategies. If applied correctly, the indicators help improving the building performance in terms of energy flexibility and can enable minimization of operational energy costs. Price-based load shifting, self-generation and self-consumption are among the most commonly used performance indicators that quantify energy flexibility and grid interaction. It has been found that the majority of performance indicators, specific to energy flexibility, are combined with rule-based control. Only a limited amount of specific energy flexibility KPIs are used in combination with optimal control or model predictive control. Both of these advanced control approaches often have a couple of economic or comfort objectives that do not take into account an energy flexibility KPI. There is evidence that recent model predictive control approaches incorporate some aspects of building energy flexibility to minimize operational cost in conjunction with time varying pricing.
  • Publication
    Quantitative evaluation of deep retrofitted social housing using metered gas data
    Research into home energy retrofit is important because most existing homes will operate in 2050. A lack of funding or incentives often prevents home energy retrofit, particularly of social housing. This study analysed retrofitted Irish social housing and their gas meter data, including pre-payment meters that require regular “top-ups” purchased from shops. The data comprised records from 100 retrofit and control group homes throughout 2013–2015. A novel evaluation of retrofitted rented homes processed meter data into multiple metrics. Gas consumption is computed per house and weather correction is incorporated, enabling statistical testing of the retrofit. A “difference in difference” technique compared the retrofit and control groups. Gas consumptions of the most popular building type are plotted as distribution curves before and after retrofit. Subsequently the energy use intensity (kWh/m2/year) is computed per home; leading to calculation of the prebound effect. In social housing, the prebound effect quantifies energy underconsumption due to self-rationing. Retrofit significantly reduced gas consumption, and reduced its variance among homes. A small positive skewness in the statistical distribution of home gas consumption prevented characterisation as a normal distribution. The prebound effect is high, but alleviated by the retrofit. Finally, retrofit extended average pre-payment intervals.
      574Scopus© Citations 9
  • 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.
      1000Scopus© Citations 41
  • Publication
    Operational characterisation of neighbourhood heat energy after large-scale building retrofit
    To achieve housing retrofit targets, traditional house-by-house approaches must scale. Neighbourhood retrofit also facilitates community participation. This paper aims to quantitatively characterise the heat energy demand of similar homes in a post-retrofit neighbourhood. The method employs the Modelica AixLib library, dedicated to building performance simulation. A modern semi-detached house is modelled as thermal network. The passive thermal network is calibrated against an equivalent EnergyPlus model. The developed Modelica model then generates time series heat energy demand to meet occupant comfort. This model separates heating for internal space and domestic hot water. Simulation results are gathered for a range of house occupancy profiles, with varying heating schedules and occupant quantities. The calibration results compare the time series of internal house temperature produced by the EnergyPlus and Modelica simulations. Modelica simulations of two heating schedules generate distinct annual demand curves against occupant quantity. As expected in a modern house, domestic hot water accounts for a relatively high proportion of heat energy. Over a year it ranges between 20% and 45% depending on occupant profile. Overall conclusions are threefold. Firstly, occupant profiles of a modern semidetached house increase annual heat energy demand by 77%, and the coincidence of daily peak demand persists across occupant profiles. Furthermore, percentages of domestic hot water demand start from 20% or 24% and plateau at 39% or 45% depending on space heating schedule. A statistical distribution of energy demand by neighbourhood homes is possible. Its curve plot is not perfectly normal, skewing to larger energy demands.
  • Publication
    Next generation building performance metrics to enable energy systems integration
    Traditional building performance metrics consider a building as a standalone and static utility consumer. Voluntary green building certifications of districts generally aggregate the metrics of standalone and consuming buildings. There is a lack of performance metrics concerning the integration of critical services to a building and the utility networks supplying these critical services of electricity, natural gas and water. In order to achieve integration of energy systems, including storage based demand side management and rain water harvesting, a methodology is modelled for a typical office. The methodology requires building parameters to be combined and manipulated in order to create the proposed performance metrics. The building model is simulated for three periods of interest: a whole year, a winter design day, a summer design day. The proposed metrics enable operational management during peak and standard loads, as well as longer term analysis of the building performance. Operational management includes the role of storage and the responsiveness of a building during demand ramping or shedding. Over the longer term, the metrics indicate efficiency trends and guide design and investment decisions. It is found that electrical storage combined with demand side management reduces energy costs with no service disruptions. Rain water harvesting is also found to significantly reduce financial and energy costs, and given its current dearth of deployment, has high future potential.