Abstract:In order to ensure the maintenance of equipment in the courts area by the power department, it is necessary to predict the load of the station area. Therefore, the power supply sector must have the ability to predict the capacity of the station load for the next year or beyond, to prevent damage to transformers due to overload, and to ensure reliable power supply to the city. The difficulty of predicting the load of Courts area lies in the prediction of the urban village, which has a large floating population, complex and diverse industrial types, and is deeply influenced by the employment environment and economic development, which shows that the change of load is more random than that of other courts areas. For this reason, we use the big data platform to predict single-factor variables, use seasonal decomposition model to seasonaldecomposition of historical electricity load, and then use linear regression and self-regression integral sliding average model (ARIMA) to predict the trend and seasonal and residual components of seasonal decomposition, respectively. To obtain a load prediction model with good precision, two characteristic industries are selected to compare and analyze its load growth characteristics.