Improved 3-D LSTM: A Video Prediction Approach to Long Sequence Load Forecasting
Jiang‐Wen Xiao, Xue-Ying Cui, Xiao‐Kang Liu, Hongliang Fang, Peng-Cheng Li
Abstract
Power load forecasting is the foundation of maintaining power grid stability, and can assist in decision-making to reduce operating costs. Fine-grained long sequence load forecasting contributes to formulating plans for power purchase, electricity consumption, energy storage, etc. Long sequence load forecasting requires models to effectively store memory and to accurately capture the long-term complex mapping between output and input. Therefore, this paper converts load sequences into three-dimensional (3D) continuous video frames and presents a model based on long short-term memory (LSTM) named the Improved 3D LSTM (I3D-LSTM) for predicting video frames. It contains two 3D LSTM units: For highly periodic load data, a Long-memory 3D LSTM unit is proposed, which has stronger long-term memory and removes short-term memory; On weakly periodic datasets, a Simplified 3D LSTM unit without the scoring parts exhibits excellent performance. I3D-LSTM also contains a 3D recurrent neural network architecture with residual. Dropblock and batch normalization are integrated into the I3D-LSTM, which are analyzed as excellent solutions for overfitting in 3D LSTM. Comprehensive tests are conducted on different sequence lengths in multiple real-world datasets. Comparison results indicate that I3D-LSTM outperforms various advanced models.