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Gaussian Process-aided Transfer Learning for Probabilistic Load Forecasting against Anomalous Events
IEEE Transactions on Power Systems ; : 1-4, 2023.
Article Dans Anglais | Scopus | ID: covidwho-2306519
ABSTRACT
A probabilistic load forecasting method that can deal with sudden load pattern changes caused by abnormal events such as COVID-19 is proposed in this paper. The deep residual network (ResNet) is first applied to extract the load pattern for the normal period from historical data. When an abnormal event occurs, a Gaussian Process (GP) with a composite kernel is utilized to adapt to the changes on load pattern by estimating the forecasting residual of the ResNet. The designed kernel enables the proposed method to adapt rapidly to changes in the load pattern and effectively quantify the uncertainties caused by the abnormal event using a few training samples. Comparative tests with state-of-the-art point and probabilistic forecasting methods demonstrate the effectiveness of the proposed method. IEEE
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Texte intégral: Disponible Collection: Bases de données des oragnisations internationales Base de données: Scopus langue: Anglais Revue: IEEE Transactions on Power Systems Année: 2023 Type de document: Article

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Texte intégral: Disponible Collection: Bases de données des oragnisations internationales Base de données: Scopus langue: Anglais Revue: IEEE Transactions on Power Systems Année: 2023 Type de document: Article