## 3 Grey Model with Seasonal Index Adjustment
### Results and Discussion  
In order to verify the effectiveness of the present prediction method, the grey model without a seasonal index adjustment (i.e., the traditional GM (1, 1)) is used as Model 1, and the grey model with a seasonal index adjustment is used as Model 2. The simulation results and accuracy test results of both models are compared. (1) Comparison of simulation results  
Figure 3 shows the comparison between the actual values and the simulation results of both models. It can be seen that the simulation result of Model 1 displays a relatively smooth curve, which cannot effectively reflect the fluctuation characteristics of the actual value; the simulation result of Model 2 is well consistent with the actual value. (2) Errors test  
Figure 4 shows the relative error of both models. It can see that the relative error of Model 2 is significantly reduced, and the average relative error of Model 2 decreases, from 0.0657 to 0.0337, in comparison to Model 1. In terms of the errors test, the simulation effect of Model 2 is better. Figure 2: Comparison the new sequence with original sequence. Correlation test  
The actual value is taken as the reference sequence, and Model 1 and Model 2 are taken as the comparison sequence. By calculating this, we can obtain the minimum range \(\Delta_{\min}=0\) and the maximum range \(\Delta_{\max}=9.82\). According to the calculation formula of the GCD, we can obtain the values of the GCD, which are 0.7587 and 0.8500 in Model 1 and Model 2, respectively; therefore, the precision of Model 2 is higher than that of Model 1. Posterior error test  
By calculating the mean variance ratio (MVR) and small error probability (SEP) formulae, we can ascertain that the MVR is \(C=0.2751\) and the SEP is \(P=0.9722\) in Model 1, and the MVR is \(C=0.1353\) and the SEP is \(P=1\) in Model 2. According to Table 1, both Model 1 and Model 2 have reached Grade 1; however, the precision of Model 2 is higher than Model 1, so  
It can be seen from Figure 5 and Table 4 that Model 2 is better than Model 1. In Figure 5, the curve of Model 2 is very close to the actual value in the first eight months, but there is some error in the data from September to December. According to the chronic of events in Suzhou of 2006 year, we found that there was a major policy adjustment in the Suzhou's Industrial Park in September 2006, which led to certain fluctuations in the industrial power consumption. It is therefore evident that Model 2 can reflect the real fluctuation. Table 4 shows that the data of the four precision indicators of the prediction values of Model 2 are also excellent. According to the judgment of the accuracy inspection grade shown in Table 1, we find that the ARE has achieved Grade II, the GCD has achieved Grade III, and the MVR and SEP have achieved Grade I, which indicates that the grey model with a seasonal index adjustment proposed in this paper has certain effectiveness in forecasting industrial power consumption. (6) Model application expansion  
We have investigated the Suzhou's industrial power consumption data of 2003-2005 from our previous project, which shows that the prediction results of the present model are excellent. In order to further verify the extrapolation capability of this present model, we also investigate the latest Suzhou's industrial power consumption data from 2019-2021. The comparison between the simulation results and the actual values is shown in Figure 6. It can be seen that the simulation results are in very good agreement with the real data, particularly in terms of the fluctuation. We also calculate the ARE and GCD, which are 0.0496 and 0.8658, respectively. The MVR is \(C\) = 0.2116 and the SEP is \(P\) = 1.