Now showing 1 - 10 of 58
  • 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
    Data-Driven Predictive Control for Commercial Buildings with Multiple Energy Flexibility Sources
    Data-Driven Predictive Control, representing the building as a cyber-physical system, shows promising potential in harnessing energy flexibility for demand side management, where the efforts in developing a physics-based model can be significant. Here, predictive control using random forests is applied in a case study closed-loop simulation of a large office building with multiple energy flexibility sources, thereby testing the suitability of the technique for such buildings. Further, consideration is given to the feature selection and feature engineering process. The results show that the data-driven predictive control, under a dynamic grid signal, is capable of minimising energy consumption or energy cost.
  • Publication
    Carbon Footprint Analysis of a Polymer Manufacturing Process
    (International Manufacturing Conference, 2011) ;
    This paper describes a carbon footprint (CF) analysis of a manufacturing process based on large scale polymer food tray production using Polyethylene Terephthalate (PET). The methodology utilised, allows for the calculation of the CF, in accordance with PAS (Publically Available Specification) 2050, of a 16.6g recycled PET (rPET) tray, based on a cradle-to-grave life cycle. Using an Irish plastics manufacturer as the experimental basis for the research, primary activity data was measured for in-house processes while secondary data was used for upstream and downstream stages. The CF of a 16.6g rPET tray was found to be 23.42 g CO2e or trays. The raw material inputs and manufacturing processes were found to contribute 45% and 33% of the greenhouse gases emissions (GHGs),respectively. The end-of-life stage was found to contribute 18% of the GHGs, while the secondary packaging and transport stages contributed only 2% each. By manufacturing the tray with 85% recycled content, the CF was found to be 60% lower compared to a benchmark based on only virgin materialutilisation. By increasing the end-of-life recycling rate from 22.5% to 32%, the CF was found to be reduced by 2%. Transport was found to have a minimal effect on CF.
  • Publication
    Sensitivity Analysis of the EPBD Energy Performance Grading of Buildings
    (Heliotopos Conferences, 2007-09) ; ;
    The Energy Performance of Buildings Directive (EPBD) obligates EU member states to develop a reliable methodology capable of calculating and certifying the energy performance (EP) of their building stock. In this paper, studies on a series of school buildings, based on Standard prEN15217:2005, consider the impact that a lack of transparency in the data gathering procedure might have on the repeatability of the EP grades. The results showed that variations in EP grades ranging from 0.06 to 1.06 EP grades were possible. The sensitivity of prEN15217: 2005 to variations of input parameters was also investigated and was found to be most sensitive to air change rates and boiler efficiency with grade changes of up to 1.5 grades possible. It was also found that prEN15217:2005 was not heavily influenced by improvements in roof and window specifications.
  • Publication
    Numerical investigation of the influence of heat exchanger U-bends on temperature profile and heat transfer of secondary working fluids
    In this paper, numerical investigations, conducted using computational fluid dynamics, on the enhancement of internal convection heat transfer following a heat exchanger U-bend under laminar flow conditions in secondary working fluids are described. Under laminar flow conditions enhanced mixing within the heat exchanger U-bends is known to occur due to the development of secondary flows, known as Dean vortices. Numerical investigations indicated that within the U-bend, secondary flows partially invert temperature profiles resulting in a significant localised decrease in average fluid temperature at the pipe surface. As a result, downstream heat transfer enhancement is observed, the magnitude of which can exceed that typical of a pipe combined entry condition in some circumstances by greater than 20% for up to twenty pipe diameters downstream. Heat transfer enhancement was found to increase with increasing U-bend radius, but to decrease with increasing heat exchanger pipe radius and internal Reynolds number
  • 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
    Investigation of demand response strategies in a mixed use building
    (Department of Civil Engineering, Aalborg University, 2016-05-25) ; ; ;
    This paper investigates demand response measures, using an EnergyPlus simulation model, developed specifically for demand response analysis, in a mixed-used commercial building. The effectiveness of various building pre-conditioning strategies, which were considered for different durations, immediacy and activation time were assessed using the simulation model. Assessment was carried out for a representative summer day and the contribution of the building capacitance as a mechanism for shifting the building electric power demand was evaluated, recording a maximum load reduction of 6.6% of the baseload.
  • Publication
    Evaluation of Machine Learning Algorithms for Demand Response Potential Forecasting
    This paper focuses on the ability of machine learning algorithms to capture the demand response (DR) potential when forecasting the electrical demand of a commercial building. An actual sports-entertainment centre is utilised as a testbed, simulated with EnergyPlus, and the strategy followed during the DR event is the modification of the chiller water temperature of the cooling system. An artificial neural network (ANN) and a support vector machine (SVM) predictive model, are utilised to predict the DR potential of the building, due to the significant amount of execution time of the EnergyPlus model. The data-driven models are trained and tested based on synthetic databases. Results demonstrate that both ANN and SVM models can accurately predict the building electrical power demand for the scenarios without or with daily DR events, whereas both predictive models are not accurate in forecasting the electrical demand during the rebound effect.
  • Publication
    Air and Water Flow Rate Optimisation For a Fan Coil Unit in Heat Pump Applications
    (Purdue University, 2012) ;
    The degradation in efficiency of auxiliary components in heating/cooling systems when operating at part load is frequently reported. Through the use of variable speed components, the supplied capacity can be reduced to match the required load and hence reduce unnecessary energy consumption. However, for fan coil units, difficulties can arisewhen optimizing fan and pump speeds at part load. Practicallylocating optimal water and air flowrates from readily available information and for varying supplied capacities is necessary,in order to reduce the fan coil power consumption. This research attempts to identify whether optimal fan and pump speeds exist for a fan coil unit and how they can be implemented, in a practical manner, in a system control applications. Using an empirical fan coil and pump model, the total power consumption (fan and pump) for different combinations of fan and pump speeds over a range of capacities was calculated. It was observed that, for a given capacity, an optimal combination of fan and pump speeds exists and there was a significant change in power consumption for different combinations of fan and pump speeds supplying the same capacity. A control strategy is described that utilizes a simple fan coil capacity estimation model, coupled with air and water flowrates, along with nominal design data. The pump speed is optimized using PID control to maintain the space temperature at the chosen set-point, which matches the supplied capacity to the required capacity. At set-time intervals, the capacity estimation model is utilized to optimize the water and air flowrates for the required capacity. The control strategy is evaluated, using a full building simulation model for a daily load profile and is compared to two baseline conditions: for no control of the fancoils/pump combination and for PID circulation control of the pump only. The optimal fan and pump speed control resulted in a 43% and 24% decrease in power consumption with compared to the no control baseline and the PID controlled circulation pump strategy, respectively.
  • 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.