## 5 Experiments
### Comparison of Different Models' Results  
#### 5.2.1 Models Introduction  
To assess the effectiveness of the proposed approach, the Gated Transformer (G-Former) was compared against various classical power prediction methods, namely Deep Neural Networks (DNN), Gated Recurrent Unit network (GRU), Temporal Convolutional Network (TCN), Vanilla Transformer (V-Former), Encoder-only, Decoder-only, Informer, and Reformer. The following is a brief introduction to each model. **DNN**: Deep Neural Network is based on linear layers structure. It consists of six blocks; each block layer consists of two linear layers. To ensure the efficiency of deep learning, a residual mechanism is added to each block. In addition, to further improve the recognition efficiency of linear layers, the Decomposition scheme used in Autoformer and FEDformer with linear layers is adopted. It first decomposes a raw data input into a trend component by a moving average kernel and a remainder (seasonal) component. Then, two one-layer linear layers are applied to each component, and sum up the two features to obtain the final prediction. By explicitly handling the trend, DNN enhances the performance of a vanilla linear when there is a clear trend in the data. **GRU**: Gated Recurrent Unit network is a kind of Recurrent neural network, which can better capture the dependencies of time series data with large intervals. It consists of an update gate and a reset gate. The reset gate determines how to combine new input information with previous memory, while the update gate defines the amount of previous memory saved to the current time step. It can better solve the gradient problems in Long-term memory and back-propagation, so it is more suitable for medium-long-term power prediction. **TCN**: Temporal Convolutional Network is a new algorithm that can be used to solve time series prediction, which has the ability to integrate modeling capabilities in the time domain and feature extraction capabilities in low parameter convolution. It is based on the convolutional model structure and uses causal convolution and residual mechanisms. It is characterized by parallel computing, flexible Receptive field, stable gradient, and variable input sequence. **VanillaTransformer**: Vanilla Transformer is a deep learning model based on the Encoder-Decoder architecture. It can learn context and meaning by tracking relationships in sequence data, such as words in a sentence. This model applies a set of evolving mathematical techniques, called attention or self-attention, to detect even subtle ways in which remote data elements in a series interact with and depend on each other. In many cases, Transformer replaces traditional classical neural network models, which is currently the most popular type of deep learning model. It has high efficiency, has strong contextual awareness, and can be parallelized for computation. **Encoder-only**: Encoder-only refers to retaining only the Encoder structure of the Transformer to complete the prediction task. It has a simple structure, faster training and inference speed, and advantages such as simple prediction, classification, annotation, and other tasks, while ensuring the same capabilities as the Transformer. **Decoder-only**: Decoder-only refers to retaining only the Decoder structure of the Transformer to complete the prediction task. Its structure is simple and avoids some difficulties in Encoder-Decoder training, such as initialization of different weights and Information bottleneck method. In the training of the Decoder-only model, the output generated in the previous step is used as the input for the next step, which achieves self-supervision of the Decoder part and is conducive to generating more coherent and structured output sequences. **Informer**: Informer is a Transformer based predictive model structure. It has a strong ability to align long time sequences and the ability to handle the input and output of long time sequences.