Abstract: To improve the accuracy of load forecasting, a load forecasting model is proposed by using correlated fuzzy neural network (CFNN) with consideration of the correlation between the historical load data. An improved artificial bee colony (ABC) algorithm is applied for the parameter identification of the model to reduce the number of fuzzy rules and decrease the complexity of the model. The model is applied to actual load forecasting, and the results show that this model has higher prediction accuracy.