基于量子和声优化的改进DMSFE组合模型及在中长期电量预测中的应用

Forecasting mid-long term electricity consumption using a quantum harmony search based improved DMSFE combination model

为了最大限度利用单项模型预测信息,减少模型选择的风险,给出了一种基于量子和声搜索算法(QHS)的改进DMSFE组合预测方法(QHS-IDMSFE)。考虑时点差异和模型差异,将DMSFE模型中的折现因子β扩展为矩阵形式。并采用量子编码和声库,利用态叠加增加和声库中每个和声携带的信息量,提高算法的寻优能力,以达到在保证MAPE目标函数最小前提下通过QHS算法寻优确定出最优β矩阵形式,进而确定单项模型的组合权重。采用两个地区年用电量数据对提出的模型进行验证,结果显示该组合方法能有效提高预测精度且适用于中长期电量预测。同时能够实现矩阵β的智能寻优,并保证预测误差最小。

A new quantum harmony search based improved discounted mean square forecast error （QHS-IDMSFE） combination model is proposed in order to combine the information of single forecasting result and reduce the risk of choosing model. Considering the influence of time difference and single model difference, the discounting factor （β） in DMSFE is extended to the matrix form. Quantum harmony is employed in Harmony Memory （HM） to increase the information of harmony based on quantum states superposition, which can effectively improve the performance of search efficiency. Thus, the bestβvalue can be determined through optimizing Mean Absolute Percent Error （MAPE） objective function by QHS algorithm. So, the corresponding weight for each single model can be determined based on optimalβvalue. The QHS-IDMSFE combination forecasting method is established and tested for annual electricity consumption prediction for two areas. The empirical analysis confirms the validity of the presented method and the forecasting accuracy can be increased in a certain degree. The proposed method is suitable to mid-long term electricity consumption prediction;meanwhile, the optimalβvalue can be determined intelligently.