Community stochastic domestic electricity forecasting

Amin, Amin ORCID: https://orcid.org/0000-0002-6891-5640 and Mourshed, Monjur ORCID: https://orcid.org/0000-0001-8347-1366 2024. Community stochastic domestic electricity forecasting. Applied Energy 355 , 122342. 10.1016/j.apenergy.2023.122342

Preview

PDF - Published Version Available under License Creative Commons Attribution. Download (5MB) | Preview

Abstract

The domestic sector is a significant energy consumer – accounting for around 40% of global electricity demand – due to household demand diversity and complexity. An accurate and robust estimation of domestic electrical loads, environmental impacts, and energy-efficiency potential is crucial for optimal planning and management of energy systems and applications. However, uncertainties resulting from simplistic socio-technical attributes, microclimatic variations, and oversimplification of the effects of interdependent variables make domestic energy modelling challenging. In this research, a hybrid bottom-up community energy forecasting framework is developed to estimate sub-hourly domestic electricity demand using a combination of statistical and engineering modelling approaches by considering key factors influencing household consumption, including demographic characteristics, occupancy patterns, and the features, ownership, and utilisation patterns of electric appliances. The framework is tested on a community in Wales, UK and validated on an annual, daily, and sub-hourly basis with monitored electricity usage averages derived from the UK Energy Follow-Up Survey and the sub-national electricity consumption datasets. Results closely reflect annual and daily household demand at individual dwellings and aggregated levels, with an estimation accuracy of up to 90%. Moreover, the framework facilitates more reliable sub-hourly demand profiles compared to conventional simulation practices that overestimate daily electricity demand and sub-hourly peaks by up to 15% and 50%, respectively.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Advanced Research Computing @ Cardiff (ARCCA) Engineering
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering
Uncontrolled Keywords:	Domestic energy; Occupancy profile; Household electricity; Energy forecasting; District simulation; Energy modelling
Publisher:	Elsevier
ISSN:	1872-9118
Funders:	European Commission
Date of First Compliant Deposit:	21 November 2023
Date of Acceptance:	12 November 2023
Last Modified:	11 Jun 2024 16:12
URI:	https://orca.cardiff.ac.uk/id/eprint/164190

Actions (repository staff only)

Edit Item