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...

Celý popis

Podrobná bibliografia

Vydané v:	IEEE access Ročník 8; s. 206039 - 206048
Hlavní autori:	Park, Kyungnam, Jeong, Jaeik, Kim, Dongjoo, Kim, Hongseok
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!