SP2LSTM: a patch learning-based electrical load forecasting for container terminal.

Short-term electricity load forecasting plays a crucial role in modern container terminal. In this work, we design a short-term forecasting approach aimed at port load under the framework of patch learning. Firstly, singular spectrum analysis is applied to obtain denoised and noise features, respectively; then, a patch learning model based on the long short-term memory network is employed to address such a time-series forecasting problem. LSTM network and BiLSTM are considered as the global models to process denoised and noisy data, respectively, and convolutional neural network is selected as the patch model. Furthermore, an endpoint detection strategy is designed for adaptively identifying the positions of patches. The performance of the proposed model is tested and verified on a real Chinese container terminal load dataset. Experimental results show that the proposed approach, compared with state-of-the-art load forecasting models, has the greatest performance with respect to seven evaluation criteria. [ABSTRACT FROM AUTHOR]