# Medium- and Long-Term Load Forecasting for Power Plants Based on Causal Inference and Informer
## 4 Overall Flow and Discussion of the Experiment
### Overall Flow of the Experiment
#### 4.2.2 Experiment 2: Model Performance Comparisons  
In Experiment 2, Causal-Informer was analyzed in comparison to four other popular time series forecasting models under equivalent conditions. As shown in Table 3, Table 4 and Figure 9, by forecasting 30 time steps and 90 time steps, the performance of all models was summarized. \begin{table}
\begin{tabular}{c c} \hline
**External Factors** & **Conditions for Binary Variables Set to 1** \\ \hline Workday & National working days \\ Servicing & Planned unit shutdown maintenance \\ Rainfall & Rainfall \textgreater{} 10 mm \\ GDP & Single-day GDP (converted from quarterly GDP) \textgreater{} RMB 30,954 million \\ Wind Speed & Wind speed \textgreater{} 6 m/s \\ \hline \end{tabular}
\end{table} 
Table 2: Binary variables design rules. Figure 8: Causal effects of various external factors on load. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **Arima** & **Prophet** & **LSTM** & **Informer** & **Causal–Informer** \\ \hline MAE & 641.54 million kwh & 717.71 million kwh & 487.34 million kwh & 507.09 million kwh & 430.59 million kwh \\ RMSE & 709.99 million kwh & 822.83 million kwh & 638.51 million kwh & 625.75 million kwh & 535.94 million kwh \\ MAPE & 31.4\% & 35.8\% & 29.9\% & 31.2\% & 24.8\% \\ \hline \end{tabular}
\end{table} 
Table 4: Evaluation metrics for all models in the same input data (Pred_length = 90 d). Figure 7: The causal effect of various temperatures on load. Figure 9: Results of Forecasting. (**a**) Pred_length = 30 d. (**b**) Pred_length = 90 d.  
Model performance is indicated by the model error values. When pred_length = 30 d in advance, Causal-Informer obtained the smallest values among all error evaluation metrics, and the Informer model obtained the second smallest values. When pred_length = 90 d in advance, Causal-Informer obtained the smallest values among all the error assessment metrics. Among the second smallest values of the three error evaluation metrics, LSTM had two and Informer had one. As can be seen from Figure 9, the performance of the different models varied considerably. Such findings could be attributed first to the difficulty of predicting load as non-stationary data and second to the difference in the predictive ability of different models. The forecast data were the plant's actual load from 1 July 2022 over the next 30 or 90 days, respectively. In early August 2022, power generation spiked due to a significant increase in electricity demand caused by continuous high temperatures. By the end of August, load decreased rapidly as temperatures dropped and power was dispatched. Beginning on 18 September 2022, one of the power plant's generating units underwent maintenance, resulting in a reduction in the plant's generating capacity. The data for the aforementioned time period were significantly different from historical data. Therefore, there were prediction difficulties. A method with robustness had to be proposed. LSTM, a widely used time series prediction algorithm, performed well on the dataset. The prediction curves follow the trend of the actual data, but there are slight discrepancies between the predicted values and the actual values. The Arima model performed poorly on this dataset. Despite debugging with various tuning methods, the model could not obtain satisfactory results. Prophet performed better when forecasting 90 days compared to forecasting 30 days. Specifically, Prophet was not able to capture the data trend during the first 25 days, but showed better performance in fitting the data trend after that period. Informer's prediction results at 30 time steps are more consistent with the real data. However, it performed poorly at 90 time steps and was weak at predicting trends. Causal-Informer successfully predicted the trend of the actual data without the effect of time lag. The predictions are close to the real data at multiple time points. A more prominent advantage is the accurate prediction of multiple turning points, which could be a good reference for power plant generation planning.