基于粒子群优化支持向量机的石油需求预测

在能源问题的研究中,石油需求的准确预测对于我国经济管理部门制定石油生产与进口计划、安排相关行业生产计划以及调整产业结构具有非常重要意义。为了实现石油需求准确预测,采用实时准确算法,提出基于粒子群优化支持向量机(PSO-SVM)的石油需求预测方法,PSO-SVM中采用粒子群优化算法优化SVM参数,以获得较优的SVM预测模型。并以我国1990~2007年石油需求数据进行测试与分析,计算实验结果表明,在石油需求预测中,PSO-SVM比BP有着更高的预测精度,为实际需求提供依据。

Prediction of Petroleum Demand Based on SVM Optimized by PSO

Accurate prediction of petroleum demand is very important to work out the plan of oil production and import,arrange production planning of relevant industry,and adjust the industrial structure.In order to predict petroleum demand accurately,the support vector machine optimized by particle swarm optimization algorithm(PSO-SVM) is proposed to predict petroleum demand in the paper.In the model,particle swarm optimization algorithm is used to determine training parameters of support vector machine,and gain the ...