General short-term load forecasting based on multi-task temporal convolutional network in COVID-19
International Journal of Electrical Power & Energy Systems
; 147, 2023.
Article
Dans Anglais
| Web of Science | ID: covidwho-2237559
ABSTRACT
The spread of the global COVID-19 epidemic has resulted in significant shifts in electricity consumption compared to regular days. It is unknown if standard single-task, single-indicator load forecasting algorithms can accurately reflect COVID-19 load patterns. Power practitioners urgently want a simple, efficient, and accurate solution for anticipating reliable load. In this paper, we first propose a unique collaborative TCN-LSTM-MTL short-term load forecasting model based on mobility data, temporal convolutional networks, and multi-task learning. The addition of the parameter sharing layers and the structure with residual convolution improves the data input diversity of the forecasting model and enables the model to obtain a wider time series receptive field. Then, to demonstrate the usefulness of the mobility optimized TCN-LSTM-MTL, tests were conducted in three levels and twelve base regions using 19 different benchmark models. It is capable of controlling predicting mistakes to within 1 % in the majority of tasks. Finally, to rigorously explain the model, the Shapley additive explanations (SHAP) visual model interpretation technology based on game theory is introduced. It examines the TCN-LSTM-MTL model's internal mechanism at various time periods and establishes the validity of the mobility indicators as well as the asynchronous relationship between indicator significance and real contribution.
Texte intégral:
Disponible
Collection:
Bases de données des oragnisations internationales
Base de données:
Web of Science
Type d'étude:
Études expérimentales
/
Étude pronostique
langue:
Anglais
Revue:
International Journal of Electrical Power & Energy Systems
Année:
2023
Type de document:
Article
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