Robust Co-Modeling for Privacy-Preserving Short-Term Load Forecasting With Incongruent Load Data Distributions

Short-term load forecasting (STLF) can support strategies for power retailers. Training based on aggregate load datasets can yield more precise STLF models. In real scenarios, however, retailers can only access the consumer information they serve. The aggregation of their data for centralized foreca...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on smart grid 2024-05, Vol.15 (3), p.2985-2999
Hauptverfasser:	Si, Caomingzhe, Wang, Haijin, Chen, Lei, Zhao, Junhua, Min, Yong, Xu, Fei
Format:	Artikel
Sprache:	eng
Schlagworte:	Anomalies Anomaly detection Artificial neural networks Clustering data incongruence Data models Data privacy Datasets Federated learning Forecasting forecasting co-modeling Load modeling Long short term memory Mathematical models model parameter security Modelling Predictive models Privacy privacy-preserving Retail stores Robustness (mathematics) Security short-term load forecasting Training
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!