灰色变异粒子群算法在电力负荷预测中的应用

由于电力负荷量是电力系统发展的基础，因此提高电力负荷量预测的准确性有利于电力系统的快速发展. 本文利用粒子群算法优化参数的良好性能和灰色预测法适合预测不确定因素影响系统的优势，提出了灰色变异粒子群组合预测模型来预测电力负荷量，提高了电力负荷预测的精度，并通过实例对组合预测模型的预测精度和有效性进行了分析. 结果表明，此组合预测模型的精度优于单一的灰色预测模型，且优于其他几种预测算法，该组合模型能很好地预测电力负荷量，为电力系统的决策和发展提供了可靠的科学数据.

Application of Grey Particle Swarm Algorithm with Mutation in Forecasting of Power Load

Due to the electric power load is the basis of development of electric power system, our work intents to improve predicting accuracy of electric power load is beneficial to the development of electric power system. This paper by using the good performance of particle swarm algorithm to optimize the parameters and the advantage of grey prediction method for forecasting uncertainty factors affecting the system puts forward the grey mutation particle swarm combination forecasting model to predict urban public transit volume, improved the electric power load accuracy. Also through the examples analyzed prediction accuracy and effectiveness of the combination forecast model. The results show that the accuracy of the combination forecast model is better than that a single gray of forecasting model and other prediction algorithms, this model can well predict urban public transit volume which provides a reliable scientific data for the decision and development of electric power system.