Community stochastic domestic electricity forecasting

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.

Automated summary

In this study, a hybrid bottom-up community energy forecasting framework is developed to accurately estimate domestic electricity demand. The framework considers key factors such as demographic characteristics, occupancy patterns, and appliance features, resulting in highly accurate estimations and more reliable demand profiles.