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...

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Bibliographic Details

Published in:	IEEE access Vol. 8; pp. 206039 - 206048
Main Authors:	Park, Kyungnam, Jeong, Jaeik, Kim, Dongjoo, Kim, Hongseok
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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 Access:	Get full text
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