Annual Peak Load Forecasting Based on Combination Model of Back Propagation Neural Network and Grey Regression
-
Abstract
In order to overcome the limitation of single power load forecasting model and improve the predicted results, an annual peak load forecasting method based on combination model of back propagation neural network and grey regression is proposed. Levenberg-Marquardt algorithm is used to optimize the parameter iterative process in back propagation neural network forecasting model; original sequence is reformed by using policy factor treatment method in the grey forecasting model, which can strengthen increasing trend of the sequence; stepwise linear regression method is used to eliminate the independent variables that have a small effect on the dependent variable in the regression forecasting model. The three forecasting models are finally weighted combined by using variance-covariance method. The combined forecasting model is tested with the actual data of Guangzhou from 2007 to 2016, the peak load of Guangzhou from 2017 to 2019 is forecasted as well. The results show that the prediction accuracy of the method proposed in this paper is relatively high and the errors are within the permissible range in engineering, indicating the method is valuable in engineering.
-
-
DownLoad: