A Temporal-Spatial Model Based Short-Term Power Load Forecasting Method in COVID-19 Context
Frontiers in Energy Research
; 10:9, 2022.
Article
in English
| English Web of Science | ID: covidwho-1883910
ABSTRACT
The worldwide coronavirus disease 2019 (COVID-19) pandemic has greatly affected the power system operations as a result of the great changes of socio-economic behaviours. This paper proposes a short-term load forecasting method in COVID-19 context based on temporal-spatial model. In the spatial scale, the cross-domain couplings analysis of multi-factor in COVID-19 dataset is performed by means of copula theory, while COVID-19 time-series data is decomposed via variational mode decomposition algorithm into different intrinsic mode functions in the temporal scale. The forecasting values of load demand can then be acquired by combining forecasted IMFs from light Gradient Boosting Machine (LightGBM) algorithm. The performance and superiority of the proposed temporal-spatial forecasting model are evaluated and verified through a comprehensive cross-domain dataset.
Full text:
Available
Collection:
Databases of international organizations
Database:
English Web of Science
Language:
English
Journal:
Frontiers in Energy Research
Year:
2022
Document Type:
Article
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