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Accurate load forecasting is of great significance for improving the level of grid planning and accurately guiding investment. In view of the shortcoming of over-fitting in the combined forecasting model of empirical risk minimization, a combined forecasting model based on social learning multi-objective particle swarm optimization algorithm was proposed in term of partial least squares regression model, support vector regression model and grey prediction GM (1, 1) model. The uncertainty function information entropy of weight was introduced to represent the expected risk, and the empirical risk and expected risk were comprehensively considered in the model. The simulation results show that the proposed method has higher prediction accuracy than the single forecasting model and the other two combined forecasting models, and the social learning multi-objective particle swarm optimization algorithm has stronger global search ability and optimization performance.

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精准的负荷预测对提高电网规划水平和准确指导投资具有重要意义。针对经验风险最小化的组合预测模型存在过拟合的缺点，提出了一种基于社会学习多目标粒子群优化算法，并利用偏最小二乘回归模型、支持向量回归模型、灰色预测GM（1，1）模型，引入权重的不确定性函数信息熵来表征期望风险，综合考虑经验风险和期望风险的组合预测模型。仿真结果表明，相比于单一预测模型和其他两种组合预测模型，所提方法具有更高的预测精度，社会学习多目标粒子群优化算法具有更强的全局搜索能力和优化性能。

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^2.Peng’an County Power Supply Branch, State Grid Sichuan Electric Power Company, Nanchong 637800, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1222963544136606346, tenantId=1146029695717560320, journalId=1205116964453384197, articleId=1222963541875876265, authorId=1222963543897531001, language=CN, stringName=彭海洋, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=^1.四川大学电气工程学院，四川 成都 610065
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彭海洋（1991-），男，硕士研究生、工程师，研究方向为负荷预测、电网规划，E-mail：842829790@qq.com

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彭海洋（1991-），男，硕士研究生、工程师，研究方向为负荷预测、电网规划，E-mail：842829790@qq.com

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