李玲玲,任琦瑛,宁楠,杨海跃

• 1:河北工业大学电气工程学院
• 2:国网河北省电力有限公司衡水供电分公司

摘要(Abstract)：

针对现有电力负荷预测方法中预测误差较大的问题，提出了一种基于改进型斑点鬣狗算法优化极限学习机（improved spotted hyena algorithm optimized-extreme learning machine,ISHO-ELM）的短期电力负荷预测模型。首先，在斑点鬣狗优化算法中引入准反向学习策略和精英策略提高算法的搜索能力，并通过基准测试函数验证了其有效性；其次，采用ISHO算法优选ELM中的随机参数以提高模型的预测精度与稳定性；最后，通过实测数据对构建的短期电力负荷预测模型的先进性与实用性进行了验证。结果表明：提出ISHO-ELM模型的拟合系数相对于已有的ELM和SVM模型分别提高了1.6%和1.7%。本研究对提高电力系统运行稳定具有重要意义。

关键词(KeyWords)： 电力负荷预测;改进型斑点鬣狗算法;极限学习机;精英策略

Abstract:

Keywords:

基金项目(Foundation): 天津市自然科学基金重点项目（19JCZDJC32100）

作者(Author): 李玲玲,任琦瑛,宁楠,杨海跃

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