The present study proposes a short-term load forecasting method based on weighted grey relational degree and improved support vector machines with the artificial bee colony algorithm (ABC-SVM). The entropy weight method was employed to obtain the weight of load-related physical information, and the historical and forecast load data selected based on the weighted grey relational degree were input into the support vector machine (SVM) to build a forecasting model. Meanwhile, the SVM parameters were optimized by the improved artificial bee colony algorithm before the model was used to perform load forecasting. The experimental results show that the proposed method could effectively improve the accuracy of the forecasting model and simplify the calculation, thus having research and practical value.

本文提出了一种基于加权灰色关联度和改进支持向量机的人工蜂群算法（ABC-SVM）的短期负荷预测方法。采用熵权法获得负荷相关物理信息的权重，并将基于加权灰色关联度选择的历史负荷和预测负荷数据输入支持向量机（SVM），建立预测模型。同时，在使用模型进行负荷预测之前，通过改进的人工蜂群算法对SVM参数进行了优化。实验结果表明，该方法可以有效提高预测模型的准确性，简化计算，具有一定的研究和实用价值。