Loughborough University
Thesis-2017-Yilmaz.pdf (7.95 MB)

Stochastic bottom-up modelling of household appliance usage to quantify the demand response potential in UK residential sector

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posted on 2017-06-20, 13:42 authored by Selin Yilmaz
This thesis presents the development of a new, stochastic bottom-up model for predicting household appliance energy demand, named the Household Appliance Usage (HAU) model. Three sub models are developed which have different functions and are based on different supporting datasets. Firstly, 2013-2014 English Housing Survey (EHS), a UK Government national representative household sample of around 17,000 homes, is chosen to provide a platform to generate the electricity demand profiles. Secondly, an appliance ownership model is developed where the nationally representative household sample is populated with electrical appliances using the appliance saturation levels derived from 2011 UK Government s Energy Follow-Up Survey (EFUS) of 3000 homes. Thirdly, an occupant behaviour model is developed where appliance behaviour metrics are simulated using monitored data from the UK Government s 2011 Household Electricity Survey (HES) of 225 homes, and electricity demand profiles are generated using the results of the appliance behaviour model. A new approach to bottom-up occupant behaviour modelling for predicting the use of household electrical appliances in domestic buildings is presented. Stochastic model predictions are made for individual households and appliances which can be used as inputs for the dynamic thermal simulation of buildings. Three metrics relating to appliance occupant behaviours are defined: the number of switch-on events per day, the switch-on times, and the duration of each appliance usage. The metrics were calculated for 1,076 appliances in 225 households in the HES sample. The analysis shows that occupant behaviour varies substantially between households, across appliance types and over time. This new modelling approach uses probability and cumulative distribution functions to capture daily variations and is based on individual households and appliances. It is shown to have advantages for modelling the variations in appliance occupant behaviours. Two minutely household appliance electricity demand profiles are generated using the appliance behaviour metrics and power demand during usage. The comparison of simulation results and measured values show that the HAU model daily power demand predictions closely match the measured data (up to 8% difference during peak time). iii The final HAU (Household Appliance Usage) model, which generates aggregate electricity demand profiles of 13,276 households that were randomly populated with appliances for 16 appliance types, is scaled up to national level using the weighting factors calculated by the 2013-2014 EHS study. The HAU model is then applied to demand shifting of individual appliance in the households to evaluate the extent that electricity demand could be shaped by time shifting. The findings provide insights about the amount of residential load that is available for shifting and a discussion is presented on the future potential of household electricity demand response. The implications of findings for policy, industry and research are discussed. The thesis discusses the design of future monitoring studies (including monitoring strategies and sample sizes) and the design of future research studies (including statistical analysis, probabilistic modelling and validation approaches) in order to further improve our understanding of and ability to predict the behaviours of occupants and their use of household appliances within buildings.





  • Architecture, Building and Civil Engineering


© Selin Yilmaz

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This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

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A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.


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