###### Abstract  
The accurate prediction of industrial power consumption is conducive to the effective allocation of power resources by power and energy institutions, and it is also of great significance for the construction and planning of the national grid. By analyzing the characteristics of the data of Suzhou's industrial power consumption between 2003 and 2005, this paper proposes a grey model with a seasonal index adjustment to predict industrial power consumption. The model results are compared with the traditional grey model, as well as the real value of Suzhou's industrial power consumption, which shows that our model is more suitable for the prediction of industrial power consumption. The lasted Suzhou's industrial power consumption data, from 2019-2021, are also investigated, and the results show that the prediction results are in very good agreement with the real data. The highlights of the paper are that all precision  
self-organizing map (SOM) and SVM, and applied it to analyzing the data of the historical energy load from the New York Independent System Operator [8]. [PERSON] et al. took wavelet transform to extract the periodic and nonlinear characteristics of power load, decomposed the power load sequence into subsequences of different frequency bands, and used an artificial neural network model make predictions according to the characteristics of each subsequence [9]. [PERSON] et al. predicted the future regional growth in electricity and weight function using the electric elasticity coefficient method [10]. [PERSON] et al. established a multiple regression model based on the screening of economic leading indicators and predicted monthly electricity consumption in Anhui Province from May 2014 to December 2014 [11]. [PERSON] et al. adopted the ARIMAX model based on co-integration relation theory to exploit self-relevant information of the internal sequence, as well as the correlation between sequences [12]. This model has a high value under the presence of a mutation structure and significant influence factors in the short-term load forecasting field. [PERSON] et al. established a hybrid model for short-term load forecasting, which combined the adaptability of Kalman filter forecasting on the basis of the time series analysis forecasting method [13]. [PERSON] et al. combined the merits of ANN and grey prediction to develop a novel load forecasting method, named grey neural network (GNN), and applied it to the monthly load demand forecasting of Shandong Province [14]. [PERSON] et al. found that the model combining the random forest algorithm with the weak time series could achieve good results in predicting the time of future power load data [15]. [PERSON] et al. developed a near-zero cooperative probabilistic scenario analysis and decision tree (PSA-DT) model to address the lacuna of an enormous predictive error faced by the state-of-the-art models [16]. [PERSON] et al. established an industrial power consumption prediction model and used residual error and posterior error to test the accuracy of the model [17]. [PERSON] et al. solved the problem faced by a single algorithm by using the grey model to fit the nonlinear growth trend of power load, and the time series model to characterize the seasonality and periodicity of the load [18]. Traditional prediction methods include the trend extrapolation method, elastic coefficient method, regression analysis method, time series method, Kalman filtering method and grey theory prediction method [19; 20; 21; 22; 23; 24]. [PERSON] et al. applied a trend extrapolation method to study the power load and found the prediction results of the second-order adaptive coefficient were better than that of quadratic exponential smoothing [19]. [PERSON] used the elastic coefficient method to predict the electricity quantity and load of the Qtaihe area [20]. In order to predict the irregular charging energy demand, [PERSON] et al. advanced a short-term load forecasting method through regression analysis [21]. [PERSON] et al.