Missing-insensitive Short-term Load Forecasting Leveraging Autoencoder and LSTM (Papers Track)
Kyungnam Park (Sogang University); Jaeik Jeong (Sogang University); Hongseok Kim (Sogang University)
Abstract
Short-term load forecasting (STLF) is fundamental for power system operation, demand response, and also greenhouse gas emission reduction. So far, most deep learning-based STLF techniques require intact data, but many real-world datasets contain missing values due to various reasons, and thus missing imputation using deep learning is actively studied. However, missing imputation and STLF have been considered independently so far. In this paper, we jointly consider missing imputation and STLF and propose a family of autoencoder/LSTM combined models to realize missing-insensitive STLF. Specifically, autoencoder (AE), denoising autoencoder (DAE), and convolutional autoencoder (CAE) are investigated for extracting features, which is directly fed into the input of LSTM. Our results show that three proposed autoencoder/LSTM combined models significantly improve forecasting accuracy compared to the baseline models of deep neural network and LSTM. Furthermore, the proposed CAE/LSTM combined model outperforms all other models for 5%-25% of random missing data.