Missing-Insensitive Short-Term Load Forecasting Leveraging Autoencoder and LSTM

In most deep learning-based load forecasting, an intact dataset is required. Since many real-world datasets contain missing values for various reasons, missing imputation using deep learning is actively studied. However, missing imputation and load forecasting have been considered independently so f...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	IEEE access Jg. 8; S. 206039 - 206048
Hauptverfasser:	Park, Kyungnam, Jeong, Jaeik, Kim, Dongjoo, Kim, Hongseok
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Datasets Deep learning Feature extraction Forecasting Load forecasting Load modeling Mathematical models Missing data missing data imputation Model accuracy Noise reduction Predictive models short-term load forecasting Time series analysis Training
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!