The impact of electrical load shifting strategies on storage capacity and service provision of domestic hot water systems in residential buildings

Domestic hot water can accounts for up to 25% of the total domestic energy consumption in the UK and is demand driven. In the UK there has been a trend to replace traditional tank systems with instantaneous supply of hot water through gas fired combination boilers, leaving the space occupied by the tank to be repurposed by the household. This space is likely to become more critical as the need for storage increases in order to participate in peak load shifting when spaceheating and hot water is supplied by electricity rather than gas. The size of tank and hence the delivery of water at an appropriate temperature delivered when demand dictate is likely to become a point of tension in future systems. This paper introduces a notion of hot water ‘service’ that can be evaluated and used to compare the performance of different systems, along side more traditional metrics such as cost, power and tank size. The analysis compares several load shifting strategies through a TRNSYS model driven by high resolution hot water data (1sec) measured in a UK home. It is demonstrated that optimal tank size and the service the system can provide are very sensitive to the load shifting strategy applied

Estimating waste heat from domestic hot water systems in UK dwellings

Domestic Hot Water (DHW) production can account for a quarter of the energy consumed in UK dwellings and this proportion is likely to increase as the energy required for space heating reduces in order to achieve demand reduction targets. As the margins for improving the performance of heating system technologies diminish, the need for improving modelling accuracy and precision increases. Although studies have considered DHW use, there is a lack of reflection on the consumption and performance of systems in contemporary UK dwellings. This paper is based on two family homes and investigates heat losses from DHW networks using high resolution demand data combined with an analytical model. The model estimates are compared to widely used building performance models and it is found that the models may over estimate the heat losses by a significant amount and that short draw-offs are particularity influential in determining the amount of heat wasted

A critical software review - how is hot water modelled in current building simulation ?

In a changing climate and with ever increasing energy standards that lead to low and zero energy buildings, the provision of hot water in buildings will become more significant in relation to the overall energy consumption. Higher demand on the provision of hot water consumption has been documented and will occur around activities such as laundry, dishwashing, food preparation, bathing and cleaning activities. The accurate prediction and simulation of hot water in building design is therefore crucial and we need to rethink how we estimate the amount of hot water in our buildings. This paper will investigate how hot water demand and provision in homes is simulated via a number of different tools. The input and output differences with respect to hot water are compared to measured data of a building in the UK

Sizing domestic air-source heat pump systems with thermal storage under varying electrical load shifting strategies

The demand for local heat storage to help manage energy demand in dwellings is likely to increase as the electrification of heat through heat pumps becomes more widespread. Sizing thermal energy storage systems has been an important topic in contemporary literature, but the effect of the electrical load shifting tariff and the service the householder receives in terms of space-heating and hot water delivered, however, has not and this is particularly important when households transition from conventional gas fired to low carbon technologies. This paper takes a whole system modelling approach to understand the impact of user demand patterns and load shifting scenarios on the volume of energy storage required for a heat-pump installation. The work uses monitoring data from several family homes to drive the simulation and finds that the level of service the householder receives is sensitive to their patterns of consumption, thermal energy storage volume and the electricity tariff, with some households being far more sensitive to tariff choice than others. The paper introduces a novel, quantifiable measure of service for space-heating and hot water systems that can be incorporated into thermal energy storage sizing procedures

Exploring the impact of model calibration on estimating energy savings through better space heating control

It is widely accepted that simulation tools need to be carefully configured with appropriate inputs to yield good estimates of building performance. Having a good representation of a building’s performance is particularly important when trying to generate a baseline against which energy savings are to be measured. This is especially challenging in residential buildings where there is a high dependency on occupant behaviour. Relevant data for domestic building is scarce and an option is to use existing guidelines published by organisations such as CIBSE or DOE. This paper considers the relative savings that might be expected by implementing several space heating control strategies, by evaluating the change in performance from a baseline model. The impact of calibrating the model on the results is given as is a description of the calibration approach used. It is demonstrated that potential energy savings can be either over or under predicted depending on the nature of the control strategy employed

Determining heat use in residential buildings using high resolution gas and domestic hot water monitoring

Residential buildings consume about a third of the UKs total energy and the need to reduce this as part of achieving the 2050 CO2 emissions targets driving the interest the modelling and performance simulation of homes. While simulation and modelling tools are in wide spread use, the detailed empirical data with which to understand the effect of systems and operational complexities of households on the consumption of energy is less developed than it is for commercial buildings. This paper reports some early results from a whole house monitoring trial in the UK where high resolution measurements of gas, hot water and power are being used to disaggregate heat use. The study has shown that: equipment used for domestic heat generation varies considerably between households; gas demand is highly variable at the sub-hourly level, far greater than some of the available hourly monitored data would suggest; and that the current information on hot water consumption characteristics is poor and so some new, more comprehensive data is presented

Modelling and calibration of a domestic building using high-resolution monitoring data

Reducing energy consumption and managing energy supply/demand responses are key challenges facing the future built environment. The use of de-carbonised electricity to deliver space heating will make significant impact on CO2 emissions for the UK. A likely technology in UK homes is to replace conventional gas boilers with heat pumps. A high coefficient of performance may mean a reduction in energy consumed, in addition the potential to contribute to demand side response through switching controlled via pricing signals. Evaluating the likely energy demand patterns from such systems and understanding how the characteristics of such systems might affect comfort can be estimated using building simulation. This paper describes the modelling and calibration process of an UK family dwelling using high-resolution monitoring data. Monitoring data describing gas, electricity, hot water, window operation and room temperature at minutely interval are used in the process

Model calibration for building energy efficiency simulation

This research work deals with an Environmental Research Institute (ERI) building where an underfloor heating system and natural ventilation are the main systems used to maintain comfort condition throughout 80% of the building areas. Firstly, this work involved developing a 3D model relating to building architecture, occupancy & HVAC operation. Secondly, the calibration methodology, which consists of two levels, was then applied in order to insure accuracy and reduce the likelihood of errors. To further improve the accuracy of calibration a historical weather data file related to year 2011, was created from the on-site local weather station of ERI building. After applying the second level of calibration process, the values of Mean bias Error (MBE) and Cumulative Variation of Root Mean Squared Error (CV(RMSE)) on hourly based analysis for heat pump electricity consumption varied within the following ranges: (MBE)hourly from -5.6% to 7.5% and CV(RMSE)hourly from 7.3% to 25.1%. Finally, the building was simulated with EnergyPlus to identify further possibilities of energy savings supplied by a water to water heat pump to underfloor heating system. It found that electricity consumption savings from the heat pump can vary between 20% and 27% on monthly bases

LEEDR project home energy dataset

<div>The LEEDR project (http://leedr-project.co.uk) was funded through the EPSRC (EP/I000267/1) between 2010 to 2014 to understand domestic energy consumption in the context of everyday family life.</div><div><br></div><div>The project engaged comprehensive, high resolution engineering based empirical monitoring (presented here) with social science and user centered design methods that employed video data, accessible from http://energyanddigitalliving.com.</div><div><br></div><div>This data set presents the monitoring data from 20 households, maintained by Richard Buswell ([email protected]), Building Energy Research Group (BERG), School of Architecture, Building and Civil Engineering, Loughborough University.</div><div><br></div><div>Please read the README.TXT file for more information.<br></div><div><br></div><div>Data was collected using building surveys and home monitoring through proprietary sensors and thorough specially developed devices. It includes:</div><div><br></div><div>- dimensioned building plans;</div><div><br></div><div>- images of key appliances;</div><div><br></div><div>- locations of sensors;</div><div><br></div><div>- building fabric and incoming energy information; and,</div><div><br></div><div>- documentation of sensor calibration and data processing procedures.</div><div><br></div><div><br></div><div>The measurements comprise:</div><div><br></div><div>- window and door opening;</div><div><br></div><div>- Passive Infra Red based room activity monitoring;</div><div><br></div><div>- electrical power measurement of incoming supply and appliances;</div><div><br></div><div>- gas consumption; and,</div><div><br></div><div>- how water consumption,</div><div><br></div><div><br></div><div>all at a sample at a sample rate of 1 minute, and additionally:</div><div><br></div><div>- gas consumption; and,</div><div><br></div><div>- how water consumption,</div><div><br></div><div>at a sample rate of 1 second.</div

LEEDR: what are the results? Participant feedback for H99

The LEEDR project was a four-year study that explored energy consumption in family homes. 20 households took part, being involved for about three years. Insights were fed back to the participants at the end of the project in the form of a unique, tailored book for each family. This book represents the style, formatting and information content of those books. This version has been called ‘H99’ and it is an amalgam of chapters from multiple homes, and therefore should not be used as a source of data or analysis: please refer to publications. The information contained here has been released generally in the hope that it might inspire and inform the development of feedback from other similar projects