基于关联模糊神经网络和改进型蜂群算法的负荷预测方法

摘要

为提高负荷预测精度,考虑历史负荷数据之间相关联的特性,利用关联模糊神经网络建立了负荷预测模型.与其他负荷预测方法相比,基于关联模糊神经网络和改进型蜂群算法的负荷预测方法,减少了模型所需要的模糊规则的数量,降低了模型的复杂度.将该方法应用于某地实际负荷预测,数值结果表明,该方法具有较高的预测精度.%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.