Research Article
A Novel Hybrid Deep Learning Model for Complex Systems: A Case of Train Delay Prediction
Table 1
Disadvantages of state prediction in some areas of real-world complex systems.
| | Prediction target | Model | Disadvantage | Reference number |
| | Building thermal load | LSTM | Weather data need to be processed separately | [22] | | Heating demand | RNN | Difficult to process long-term thermal load | [24] | | Thermal load of district heating networks | DNN | The attribute of characteristic should be considered | [25] | | Building energy consumption | GRU | Only classify the building energy data into time categories | [26] | | Numerical weather prediction | DNN | The spatial characteristic of the grid point should be mined by the model | [27] | | Storm surges | CNN_LSTM | No model is built for spatial characteristics | [28] | | PM2.5 concentration | CNN_LSTM | PM2.5 influencing factors need to be distinguished | [29] | | Temperature and wind speed | 3D CNN_FNN | The time characteristics of dynamic systems should be considered separately | [30] | | Useful life of the complex system | LSTM | The efficiency of the model could be improved | [31] | | Thermal and cooling consumption of building | 1D CNN | The model’s ability to process time series data should be improved | [4] | | Train delay | DNN | External influence should be further analyzed | [32] | | Train delay | LSTM | Spatial relationships among different stations should be considered | [33] | | Train delay | LSTM | Time sequence characteristics mining of train influencing factors should be improved | [34] |
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