According to the characteristics of short-term electric power load data has obvious cyclical, the machine learning is introduced into the short-term power load forecasting field, and a the RBF neural network short-term power load forecasting method based on ridge regression estimates is proposed, the method using the advantages in nonlinear fitting of RBF machine learning algorithm, combined with ridge regression parameters to estimate RBF neural network output layer weights, effectively eliminate the input multicollinearity problem, the generalized cross-validation method is adopted to evaluate the load forecasting model, to find the optimal ridge parameter, improve the power load forecasting accuracy.By comparing the actual load forecasting case with the traditional BP neural network load forecasting method, it is verified that the power load forecasting method proposed in this paper has better stability and higher prediction accuracy than the traditional method.
