A method of formulating energy load profile for domestic buildings in the UK
Introduction
In the UK, the non-domestic building sector contributes 19% and the domestic sector 27% to total UK CO2 emissions, hence buildings are critically important to the UK national response to climate change [1], [2]. Britain will aim for a 60% reduction in carbon dioxide emissions by 2050 as part of efforts to curb global warming, according to government energy white paper [3]. Renewable energy (RE) is expected to play an important role in meeting the target of 60% reduction in carbon dioxide emissions by 2050. Considerable effort is being directed to the deployment of RE technologies in an attempt to mitigate greenhouse gas emissions. The diversity of load demands at macro scale are such that there will always be an intermittent demand ideally matched to the power available from the RE systems. At the macro scale, the power ratings of the installed RE systems are substantially greater fraction of the demand. In this case, the optimum match between demand and supply becomes crucial. Therefore at RE system strategic design stage, a very important element is to determine energy demands based on the consumption patterns of individuals. The other important issue in sizing renewable energy system is the load profile. The varieties of factors will determine the energy demand. The attainment of the optimum mix of measures and renewable system deployment requires a simple method suitable for use at the early design stage.
The aim of this study is to develop a simple method of predicting households daily energy-consumption profile for planning and strategic design of RE system for residential buildings in the UK.
Section snippets
Methodology
In order to match load shape to the RE power generated by local RE system, it is essential to identify the pattern of energy uses of a house and to predict domestic load profile. In 2000, 56% of UK domestic energy-consumption was used for space heating, 24% for domestic hot water, 20% for lighting and appliance [1]. The use pattern varies depending on the different factors, such as climate, household composition, family income, culture background, and human factor, etc. In order to produce
Heating load
Load profile for space heating depends on the building thermal characteristics, orientation, internal air temperature control, and local climate, etc. The space heating load has been simulated using thermal resistant method based on the energy balance [9], [10]. The equations can be illustrated as follow:C is the thermal capacity of the stated node, Φheat/cool the auxiliary heating/cooling energy of the room, Φcond the conductive heat transfer through the
Example of UK average household
An example of a typical load profile for UK average size household has been performed using the method presented above. The daily energy-consumption load profiles of electric appliance, DHW and space heating have been calculated for a winter weekday case.
Fig. 4 shows the modeling results of a UK average household appliance load profiles. The thin line represents specific appliance load profile of each occupancy scenario and the thick line represents the typical appliance load profile.
Fig. 5
Thermal model
The space heating load profile was produced using the thermal model developed at the Martin Centre [9], [10]. The model has been validated by the well-validated simulation software Esp-r.
Appliance model
The UK Electricity Associate Load research Team carried out load profile research and the results are based on the sample of 1300 customers. We take a 3-person winter weekday profile to compare with the model method proposed in this paper. The regional electric load profile of a 100 households in a proposed
Interface
In order to make the method to be used easily and feasible to any users, the computer interface has been developed (see Fig. 11). This will enable the designers to predict individual house, community or regional electricity load profile so that for the RE system planning and design.
The input data are categorized as:
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Location of the community.
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Domestic households information, such as the number of persons in the family, un-occupied period, activity period, etc.
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Appliance usage information, such as
Conclusion
This paper introduced a simple method of prediction of daily load profile (SMLP). Cluster analysis method has been applied based on the proposed scenarios of occupancy patterns. The method can be applied at both macro (national, regional) and micro (individual houses) levels. To calculate the electric appliance load profile, the required input data are daily average end-use energy-consumptions. To calculate domestic hot water profile, the required input data is daily average hot water
Acknowledgements
The work of this paper is originally based on the project ‘Microgrids—Distribution on-site Generation’ funded by the UK Tyndall Centre. The further development of computer-based version is funded by Cambridge-MIT Institute ‘Sustainable Building Design’ project. The authors appreciate the administrative support from Samantha Lawton and the assistant from visiting Ph.D. student Mr. Qing Luo.
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