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Large load forecasting is an important basis for efficient power system planning and operation of power companies. In order to improve the accuracy of power load forecasting and then estimate power metering and billing more effectively, a novel method of power bulk load metering and billing forecasting based on improved adaptive Kalman Filter (AKF) is proposed. The research status of power load forecasting is analyzed. Aiming at the shortage of traditional Kalman filtering algorithm, the adaptive forgetting factor is introduced to improve the Kalman filtering algorithm. The mathematical model and the key parameters of the setting factor adjustment model are established to obtain the predictive value of power load data. Finally, the power consumption and the predictive value of power charge metering are obtained through the metering and billing conversion formula. The simulation results show that the error between the prediction results and the actual results of the AEKF based power heavy load metering prediction method is less than 1.35%. The application example shows that the AEKF based method for estimating the metering and charging of large load of electric power can improve the dispatching efficiency of electric power companies by 12% and increase the revenue of electric charges by 5.3% - 12.2%.