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Multi-energy models of residential energy demand: further developments and international comparisons

School of Engineering

About the Project

Several researchers have developed bottom-up simulation models (Load Profile Generators, LPGs) for residential buildings in various international contexts such as the UK (CREST/CHAP), Germany, Sweden, and Japan. With the exception of the CREST and CHAP models, these are not open source. Furthermore, these approaches suffer from several weaknesses due to the way in which they employ Time of Use Surveys to derive activities profiles, which are then related to consumption profiles. For example, models employing Markov chains typically only employ first order ones, despite the fact that higher order chains would be more appropriate for many activities. The activities from the TUS are associated with specific appliances that are stochastically activated to produce energy demand profiles, but this allocation of activities to groups as well as groups to appliances is somewhat arbitrary and results in several limitations in the models results. In addition, the use of TUS only allows a very rough socioeconomic differentiation between archetypes, due to the problem of small sample sizes when disaggregating based on multiple features. Furthermore, these approaches are poor at considering parallel activities with multiple occupants, do not allow longer sequences to be simulated, which are particularly important in the context of RES integration studies with storage devices, where longer, internally-consistent demand profiles are required. These models also tend to focus on only one energy carrier (e.g. power) or where they do consider several, these are typically not internally consistent (e.g. SynPro), whereby CHAP is an exception here. Within this PhD project these models should be further developed to include the following central innovations:

• Depth of energy demand insights on the demand side, i.e. granularity in e.g. households will be further increased. For example, an LPG (SYNPRO-emobility) was co-developed by the Main Supervisor for the German context in order to generate EV charging profiles. The level of socioeconomic differentiation could be improved in this project. In addition, demand fractions classified as technically-flexible or non-flexible will be incorporated into these model developments in order to facilitate analyses of load shifting potentials etc.
• Improving sequences of activities over days and weeks: increased flexibility within the energy system is required in order to effectively integrate large volumes of renewable energy. One integration measure is energy storage, which exists in various forms with different techno-economic characteristics, for example the storage volume and time duration. In order to perform detailed energy system analyses of highly-renewable systems with storage, long time series of load profiles are required, which are typically not provided by state-of-the-art LPGs.
• Improving these models in the international dimension: open source models have been co-developed for the UK (CREST and CHAP) and Germany with members of the PhD supervision team, and are in development by the Main Supervisor for Austria and Denmark. Additional countries will be considered, based on data availability and the precise locations of the interventions, once the project commences.
• Exploit smart meter datasets to provide rich paramterisation for bottom up models: Hence in order delve deeper into the socioeconomic characteristics of the demand side, this PhD project will define a consistent set of energy archetypes in order to reduce the complexity and heterogeneity of the system being analysed (“the demand side”). The archetypes will be defined according to important socioeconomic parameters relating to individuals, households and buildings

Candidates should have (or expect to achieve) the UK honours degree at 2.1 or above (or equivalent) in Engineering, Mathematics, Energy Engineering, Industrial Engineering (and Management). It is essential that the applicant has a background in Energy Systems Modelling, Programming, Geographical Information Systems (GIS), Optimization, Simulation along with knowledge of MATLAB, GAMS, ArcGIS, R, Python, Java


• Apply for Degree of Doctor of Philosophy in Engineering
• State name of the lead supervisor as the Name of Proposed Supervisor
• State ‘Self-funded’ as Intended Source of Funding
• State the exact project title on the application form

When applying please ensure all required documents are attached:

• All degree certificates and transcripts (Undergraduate AND Postgraduate MSc-officially translated into English where necessary)
• Detailed CV

Informal inquiries can be made to Professor R McKenna (), with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Postgraduate Research School ()

It is possible to undertake this project by distance learning. Interested parties should contact Professor McKenna to discuss this. Distance Learning applicants should have access to a good quality computer

Funding Notes

This project is advertised in relation to the research areas of the discipline of Engineering. The successful applicant will be expected to provide the funding for Tuition fees, living expenses and maintenance. Details of the cost of study can be found by visiting View Website. THERE IS NO FUNDING ATTACHED TO THIS PROJECT


• McKenna, R., Hofmann, L., Merkel, E., Fichtner, W. and Strachan, N. (2016) ‘Analysing socioeconomic diversity and scaling effects on residential electricity load profiles in the context of low carbon technology uptake’, Energy Policy, vol. 97, pp. 13–26.
• Zhang, T., Siebers, P.-O. and Aickelin, U. (2012) ‘A three-dimensional model of residential energy consumer archetypes for local energy policy design in the UK’, Energy Policy, vol. 47, pp. 102–110.
• Mata, É., Sasic Kalagasidis, A. and Johnsson, F. (2014) ‘Building-stock aggregation through archetype buildings: France, Germany, Spain and the UK’, Building and Environment, vol. 81, pp. 270–282,
• McKenna, R. et al. (2013) ‘Energy efficiency in the German residential sector: A bottom-up building-stock-model-based analysis in the context of energy-political targets’, Building and Environment, vol. 62, pp. 77–88.
• McKenna, R., Kleinebrahm, M., Yunusov, T., Lorincz, M. J., Torriti, J. (2018): Exploring socioeconomic and temporal characteristics of British and German residential energy demand, Paper presented at the BIEE Annual Conference “Consumers at the Heart of the Energy System?”, September 2018, Oxford, UK.
• McKenna, E., Thomson, M. (2016): High-resolution stochastic integrated thermal–electrical domestic demand model, Applied Energy, 165, 445-461
• Ramirez-Mendiola, J., Grunewald, P. and Eyre, N. (2019) Residential activity pattern modelling through stochastic chains of variable memory length. App. Energy, 237: 417-430.
• Fischer, D., Harbrecht, A., Surmann, A., McKenna, R. (2019): Electric vehicles‘ impacts on residential electric load profiles – A stochastic modelling approach considering socio-economic, behavioural and spatial factors, Applied Energy, 233-234, 644-658

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