Analyzing the impact of COVID-19 on the electricity demand in Austin, TX using an ensemble-model based counterfactual and 400,000 smart meters

Analyzing the impact of COVID-19 on the electricity demand in Austin, TX using an ensemble-model based counterfactual and 400,000 smart meters

Abstract The COVID-19 pandemic caused lifestyle changes and has led to the new electricity demand patterns in the presence of non-pharmaceutical interventions such as work-from-home policy and lockdown. Quantifying the effect on electricity demand is critical for future electricity market planning y...

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Detalhes bibliográficos
Principais autores: Ting-Yu Dai, Praveen Radhakrishnan, Kingsley Nweye, Robert Estrada, Dev Niyogi, Zoltan Nagy
Formato: Artigo
Idioma:English
Publicado em: Springer 2023-05-01
coleção:Computational Urban Science
Assuntos:
COVID-19
Machine learning
Data synthesizer
Socioeconomic factors
Building energy performance simulation
Ensemble model
Acesso em linha:https://doi.org/10.1007/s43762-023-00095-w

Internet

https://doi.org/10.1007/s43762-023-00095-w

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