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文章摘要
基于耦合GPR-PSO的北京地区中长期电力需求预测
Medium and long-term power demand forecasting in Beijing based on coupled GPR-PSO
Received:October 31, 2019  Revised:October 31, 2019
DOI：10.19753/j.issn1001-1390.2020.02.012
中文关键词: 高斯过程回归，粒子群算法，电力需求预测，神经网络训练
英文关键词: 
基金项目:
Author NameAffiliationE-mail
huangyuansheng Baoding, North China Electric Power University lfjnmg1969@126.com 
hujianjun* Baoding, North China Electric Power University nxhujianjun918@sina.com 
caiyaqian Baoding, North China Electric Power University 625997096@qq.com 
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中文摘要:
      建立科学合理的中长期电力需求预测方法，是电力产业科学规划建设的前提。本文构建了基于高斯过程（GPR）和粒子群（PSO）的混合电力需求预测模型。论文采用PSO算法对协方差函数中的参数进行优化，将修正后的参数作为初始值在GPR模型中进行电力需求方面的培训。在贝叶斯框架下，对协方差函数中的参数再次进行优化。最后用训练好的GPR模型进行电力需求预测，并将结果与自回归积分移动平均模型和指数平滑模型进行比较。验证结果表明，基于高斯过程（GPR）和粒子群（PSO）的混合电力需求预测模型具有很好的稳定性和更高的预测精度。
英文摘要:
      
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