CCHP用户冷热电负荷预测的纵横交叉优化深度信念网络方法

CSO Optimized Deep Belief Network Based Method for Cooling Heating and Power Load Forecasting of CCHP Users

针对目前CCHP用户冷热电负荷预测影响因素繁多、构建模型复杂、预测精度不足的问题,提出了一种结合变分模态分解和深度信念网络的CCHP用户冷热电负荷预测组合预测方法。首先分析了CCHP用户冷热电负荷序列的周期性和随机性,提出采用变分模态分解方法对CCHP用户冷热电负荷进行分解;其次,基于分解后模态分量易于出现冗杂,采用样本熵对分解后模态分量进行重构,降低冗杂程度;最后,因深度信念网络初始权重过于随机化,采用纵横交叉算法优化深度信念网络对CCHP用户冷热电负荷进行预测,并依据实例仿真,分析了CCHP用户冷热电负荷的预测结果。实例表明,所提预测方法有效地提高了预测精度,实用性强。

Aiming at the problems of various influencing factors,complex modeling and insufficient prediction accuracy,a combined forecasting method of energy demand based on variational modal decomposition and deep belief network is proposed.Firstly,the periodicity and randomness of the cooling and heating load series are analyzed,and the variational modal decomposition method is proposed to decompose the cooling and heating load.Secondly,based on the fact that modal decomposition are prone to be redundant after decomposition,the sample entropy is used to reconstruct the decomposed mode components to reduce the degree of redundancy.Finally,because the initial weights of the deep belief network are too random,crisscross optimization algorithm is adopted.The cooling and heating load is predicted by optimizing the deep belief network,and the prediction results are analyzed according to the simulation example.The example shows that the proposed prediction method can effectively improve the prediction accuracy and is practical.