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 2020, Vol.8, p.206039-206048
Main Authors: Park, Kyungnam, Jeong, Jaeik, Kim, Dongjoo, Kim, Hongseok
Format: Article
Language:English
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
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Summary: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 far. In this article, we provide a deep learning framework that jointly considers missing imputation and load forecasting. We consider a family of autoencoder/long short-term memory (LSTM) combined models for missing-insensitive load forecasting. Specifically, autoencoder (AE), denoising autoencoder (DAE), convolutional autoencoder (CAE), and denoising convolutional autoencoder (DCAE) are considered for extracting features, of which the encoded outputs are fed into the input of LSTM. Our experiments show that the proposed DCAE/LSTM combined model significantly improves forecasting accuracy no matter what missing rate or type (random missing, consecutive block missing) occurs compared to the baseline LSTM.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3036885