Abstract：China has the world's largest rail transit network, with a total mileage of more than 150,000 kilometers, the electrification rate of more than 70 percent, and railway energy consumption is the largest single load category. With the rapid development of China's electrified railway network, the railway transportation represented by high-speed rail has become an important part of China's transportation system. With the development of "net-source-storage-vehicle" collaborative energy supply technology, the internal energy management and collaborative control of the system need the technical support of accurate second-level ultra-short-term prediction on both sides of the source and load. At the same time, accurate ultra-short-term prediction of traction load can also provide data source support for research on power quality analysis of rail transit, optimal scheduling of traction substation, location and capacity determination of traction substation, etc. This paper constructs an ultra-short-term forecast framework for traction load based on EEMD-CBAM-BiLSTM, aiming at the problem of difficulty in predicting rail transit traction load due to strong mutability and volatility. Firstly, based on the analysis of the time series characteristics of rail transit traction load, the data of rail transit traction load is decomposed into several stable and regular time series mode functions by ensemble empirical mode decomposition (EEMD) to highlight the time series characteristics of load data. Secondly, the decomposed components are integrated into the feature extraction module composed of convolutional neural network (CNN) and improved convolutional block attention module (CBAM) to extract the global timing features. After that, Bayesian optimization (BO) is used to search the optimal parameters of BiLSTM neural network to make the network structure reach the best state.In this paper, the traction load data in a single day (with a resolution of 1 s) is selected to build a prediction model, and the model is compared with the mainstream single prediction model and composite model by starting from three output modes: predicting the load in the next 4 seconds, the load in the next 8 seconds, and the load in the next 16 seconds. The calculation results show that compared to other models, the framework in this paper reduces MAE by 31.2%, 39.1%, 47.5%, and 45.8% in the 40-16 prediction mode, respectively; RMSE decreased by 40.0%, 42.6%, 53.6%, and 47.5% respectively; R2 increased by 5.2%, 5.9%, 10.65%, and 7.7% respectively. Therefore, the following conclusion can be drawn: EEMD has good performance in avoiding modal aliasing and better highlighting the deep data relationships and temporal features hidden in time series. The improved CBAM feature extraction module and global feature extraction structure can significantly reduce the computational workload caused by the "decomposition prediction" method, improve the representation ability of hidden temporal features in traction load data, better capture key features in the data, and improve the accuracy of subsequent neural network predictions. After Bayesian optimization of hyperparameters, the BiLSTM model can better capture information in the front and back temporal directions when processing long feature sequences, fully considering the past and future temporal information based on the current point as the reference point. This article framework combines the advantages and characteristics of EEMD, CBAM, CNN, BO, and BiLSTM technologies, and can significantly improve the accuracy of ultra short term prediction of electrified railway traction loads at various prediction steps, which has good engineering application value.
钟吴君, 李培强, 涂春鸣. 基于EEMD-CBAM-BiLSTM的牵引负荷超短期预测[J]. 电工技术学报, 2024, 39(21): 6850-6864.
Zhong Wujun, Li Peiqiang, Tu Chunming. Traction Load Ultra-Short-Term Forecasting Framework Based on EEMD-CBAM-BiLSTM. Transactions of China Electrotechnical Society, 2024, 39(21): 6850-6864.
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