蒋敏;顾东健;孔军;田易之;

• 1:轻工过程先进控制教育部重点实验室(江南大学)
• 2:新疆大学电气工程学院

摘要(Abstract)：

由于电力市场的发展和智能电网技术的推广,负荷预测变得越来越重要。准确的预测结果有助于提高电力系统运行效率,降低运行成本,减少"电荒"事件的发生。在当前海量高维数据的背景下,有效并准确的在线预测方法是当下的研究重点。针对传统预测方法对新增数据需要重复训练造成的巨大计算消耗和模型利用率低的缺点,提出了一种基于在线序列极限支持向量回归算法(online sequential extreme support vector regression,OS-ESVR)的短期负荷预测模型(short-term load forecasting,STLF)。首先,利用基于随机森林模型的递归特征消除方法(recursive feature elimination based on random forest,RF-RFE)自动选择滞后负荷输入变量;其次,将得出的有效数据信息输入到在线序列支持向量回归模型进行训练学习,训练过程中通过简化粒子群算法(simplified particle swarm optimization,SPSO)对初始模型进行优化,得到训练后的在线序列支持向量回归模型;最后,利用测试数据测试模型。通过在新英格兰ISO(Independent System Operator)数据集进行仿真算例分析,验证了模型能够根据新增数据动态更新。同时预测结果表明了所提模型的时效性和准确性显著优于已有的同类方法。

关键词(KeyWords)： 短期负荷预测;递归特征选择方法;在线序列极限支持向量回归模型;简化粒子群算法

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(61362030);; 新疆维吾尔自治区科技支疆项目计划(2017E0279)~~

作者(Author): 蒋敏;顾东健;孔军;田易之;

Email:

DOI: 10.13335/j.1000-3673.pst.2017.2400

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