Now showing 1 - 10 of 58
  • Publication
    Mathematical Modelling of a Low Approach Evaporative Cooling Process for Space Cooling in Buildings
    (The International Institute of Refrigeration, 2012) ; ;
    This paper describes a mathematical model of a low approach open evaporative cooling tower for the production of high temperature indirect cooling water (14-16°C) for use in building radiant cooling and displacement ventilation systems. There are several potential approaches to model evaporative cooling, including: the Poppe method, the Merkel method and the effectiveness-NTU (ε-NTU) method. A common assumption, applied to the Merkel and ε-NTU methods, is that the effect of change in tower water mass flow rate due to evaporation is ignored, which results in a simpler model with reduced computational requirements, but with somewhat decreasedaccuracy. In this paper, a new improved method, called the corrected ε-NTU approach is proposed, where the water loss due to evaporation is taken into account. It is expected by this correction the results of improved ε-NTU in the category of heat transfer will be more close to the results ofmore rigorous Poppe method.The current mathematical model is evaluated against experimental data reported for anumber of open tower configurations, subject to different water temperature and ambient boundary conditions. It is shown that the discrepancies between the calculated and experimental tower outlet temperatures are to within ±0.35°Cfor a low temperature cooling water process (14-16°C), subject to temperate climate ambient conditions and ±0.85°C for a high temperature cooling water process (29-36°C),subject to continental climate ambient conditions.Considering the associated tower cooling loads, predicted results were found to be within a 6% root-mean-square differencecompared to experimental data.
  • Publication
    The transparency and repeatability of building energy performance certification
    The European Union’s (EU) Energy Performance of Buildings Directive (EPBD) aims to increase the energy performance (EP) of buildings by requiring EU Member States to develop an EP calculation methodology and to certify the EP of their buildings. Dynamic simulation offers an important means of developing accurate EP ratings. However, its value may be undermined due to the difficulty in obtaining transparent and repeatable input data on existing buildings. Using the EnergyPlus simulation engine and a DesignBuilder interface this research investigated the impact of this difficulty on the EP grade of four primary school buildings. Survey and on-site refinement phases enabled base case buildings to be modelled, a standard activity schedule to be developed and the lack of transparency and repeatability in the specification of the infiltration air change rate (ACH), the boiler efficiency and glazing parameters to be seen. Using parametric sensitivity analysis in combination with the draft European standard for the energy certification of buildings, prEN15217:2005, it was found that variations in the specification of these parameters could lead to up to two grade changes for boiler efficiency and ACH, one grade change due to the sensitivity of the glazing parameter and three grade changes should their affect be combined. Although repeatability and transparency can be improved through careful training of building EP assessors and the awareness of a particular parameters affect on an EP grade, it will be difficult to ensure repeatability and transparency using a dynamic simulation EP grading methodology if experimental testing is not utilised.
  • 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
    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.
  • Publication
    A Novel Hybrid Technique For Building Demand Forecasting Based On Data-driven And Urban Scale Simulation Approaches
    This paper presents a novel feature engineering procedure to generate case study specific input variables for the training of data-driven models used to predict the heating demand of blocks of buildings. Traditionally, predictive model training is performed using sets of data from sensors (e.g. weather stations, metering systems). Feature engineering procedures such as the inclusion of innovative predictive variables in the forecasting framework are generally not considered. The method presented in this paper exploits results of calibrated physics-based building energy models that are included as an additional independent variable in combination with the traditional sets of predictors in an innovative forecasting framework. The method is tested on a district case study of the city of Geneva (CH) served by a district heating network. Results show that the presented approach improves the quality of the forecasting outcomes of state-of-the-art predictive algorithms. In this context, the accuracy of the simulation outputs affects the predictive capability of the presented forecasting procedure. In addition, normalised information derived from substation of the heating network of the district are informative for the predictive model.
  • Publication
    Evaluation of Defrost Options for Secondary Coolants in Secondary Loop Multi-temperature Transport Refrigiration Systems - Mathematical Modelling & Sensitivity Analysis
    This paper describes a mathematical model of the defrost process for a finned-tube air chiller, utilised as a heat exchanger in asecondary loop multi-temperature transport refrigeration system, where an antifreeze mixture is deployed as a sensible secondary working fluid. Two defrost modes are modeled: an electric mode which effects defrost by localised resistance heating of the chiller secondary working fluid, and a hot gas primary circuit mode that indirectly heats the secondary working fluid by means of a primary to secondary heat exchanger. The model, which was implemented using the Engineering Equation Solver (EES), is based on a finite difference approach to analyse the heat transfer from the secondary working fluid, through a single finned heat exchanger section, to the frost. An iterative scheme is used to integrate for the overall heat exchanger, taking into account temperature glide associated with the secondary working fluid. The overall heat exchanger model is incorporated within a system defrost model, which allows the entire defrost process to be modeled. The model was validated for the standard United Nations Agreement on Transportation of Perishable Produce (ATP) for cold room set-points of 0C, -10C and -20C, by comparison with experimental data from a full scale laboratory based test programme. The validated model is used to carry out defrost sensitivity studies which examine defrost behavior for a range of performance parameters.
  • 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.
  • 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
    Towards Robustness of Data-Driven Predictive Control for Building Energy Flexibility Applications
    Identifying physics-based models of complex dynamical systems such as buildings is challenging for applications such as predictive and optimal control for demand side management in the smart grid. Data-driven predictive control using machine learning algorithms show promise as a more scalable solution when considering the greater building stock. The robustness of these algorithms for different climate data, building types, quality and quantity of data, is still not yet well understood. The objective in this study is to investigate model identification and the resultant accuracy for these various contexts using the `separation of variables' technique (DPC-En) and the consequent performance implications of the data-driven controller. The DPC-En controller is tested using a closed-loop simulation testbed of a `large office' archetype building. The results show that the technique is relatively robust to missing data and different climate types and delivers promising results using limited training data without the need for disruptive excitation measures. This work contributes to enabling a greater proportion of the diverse building stock to be utilised for demand side management by harnessing their inherent energy exibility potential.
  • Publication
    A Generic Energy Flexibility Evaluation Framework to Characterise the Demand Response Potential of Residential Buildings
    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.