基于改进遗传算法的电网投资组合预测方法

提出了一种通过改进遗传算法并综合利用灰色预测GM(1, N)模型、BP神经网络模型、多元回归模型建立的电网投资组合预测模型。基于传统遗传算法对组合预测约束条件进行了优化并改进了遗传算法中交叉算子和变异算子,从而使算法具有更强的全局搜索能力和收敛能力。利用所提出的组合预测模型对某地区电网投资进行预测的结果表明,相比于单一预测模型和其他两种组合预测模型,所提组合预测模型能充分利用原始数据的信息,具有更高的预测精度。

Power grid portfolio forecasting method based on an improved genetic algorithm

This paper proposes a grid portfolio forecasting model established by an improved genetic algorithm and comprehensively using the grey prediction GM (1, N), BP neural network and multiple regression models. The traditional genetic algorithm is used to optimize the combined prediction constraints and improve the crossover operator and mutation operator in the genetic algorithm, so that the algorithm has stronger global search and convergence ability. The prediction results of a regional grid investment using the combined forecasting model proposed in this paper show that compared with the single forecasting model and the other two combined forecasting models, the proposed model can make full use of the original data and has higher prediction accuracy. This work is supported by National High-tech R & D Program of China (863 Program) (No. 2015AA050101).