This paper describes the extension of CREST's existing electrical domestic demand model into an integrated thermal-electrical demand model. The principle novelty of the model is its integrated structure such that the timing of thermal and electrical output variables are appropriately correlated. The model has been developed primarily for low-voltage network analysis and the model's ability to account for demand diversity is of critical importance for this application. The model, however, can also serve as a basis for modelling domestic energy demands within the broader field of urban energy systems analysis. The new model includes the previously published components associated with electrical demand and generation (appliances, lighting, and photovoltaics) and integrates these with an updated occupancy model, a solar thermal collector model, and new thermal models including a low-order building thermal model, domestic hot water consumption, thermostat and timer controls and gas boilers. The paper reviews the state-of-the-art in high-resolution domestic demand modelling, describes the model, and compares its output with three independent validation datasets. The integrated model remains an open-source development in Excel VBA and is freely available to download for users to configure and extend, or to incorporate into other models.
Funding
This work was supported by the Engineering and Physical Sciences Research Council, UK, within the Top and Tail of Energy Networks project (EP/I031707/1).
History
School
Mechanical, Electrical and Manufacturing Engineering
Research Unit
Centre for Renewable Energy Systems Technology (CREST)
Published in
Applied Energy
Volume
165
Pages
445 - 461
Citation
MCKENNA, E. and THOMSON, M., 2016. High-resolution stochastic integrated thermal-electrical domestic demand model. Applied Energy, 165, pp. 445-461.
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/
Acceptance date
2015-12-19
Publication date
2016-01-06
Copyright date
2016
Notes
This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/