电量预测是供电单位购电的重要依据，是电力经济稳定运行的根本基础。提出采用SARIMA-ARCH融合模型 对某地区售电量进行预测。首先，采用季节时间序列分析方法（SARIMA）对月度电量时间序列进行建模，通过 ACF和 PACF 图筛选确定出最佳模型阶数，得到季节性时间序列模型（SARIMA）基础预测模型；提取模型残差的波动性，建立自回归条件异 方差（ARCH）模型；最后，将SARIMA-ARCH 模型与常规SRIAM和ARIMA的预测值进行对比分析。结果表明，SARIMAARCH混合模型的预测精度较高。

Electricity quantity prediction is an important basis for power supply units to purchase electricity, and it is the fundamen‐ tal foundation for the stable operation of the electricity economy. This article proposes using the SARIMA-ARCH fusion model to predict electricity consumption. Firstly, the seasonal time series model (SARIMA) is used to model the monthly electricity consump‐ tion time series. The optimal model order is determined through ACF and PACF graph screening, and the basic prediction model of the seasonal time series model (SARIMA) is obtained. Subsequently, the regression residuals of the basic model are subjected to ARCH effect testing, and an autoregressive conditional heteroscedasticity (ARCH) model is established. Finally, this article com‐ pares and analyzes the predicted values of the SARIMA-ARCH model with those of conventional SRIAM and ARIMA. The results show that the prediction accuracy of the hybrid model of SARIMA-ARCH is high.