Accurate power load forecasting contributes to guaranteeing safe dispatch and stable operation of a power system. As a great forecasting tool, support vector machine is widely used in power load forecasting. However, due to the rapid development of information technology, the prediction result of simple support vector machine is no longer accurate enough to forecast in the smart grid. To enhance the prediction accuracy, this paper makes some improvements on support vector machine, and proposes a hybrid model integrated with intelligent methods for feature selection and parameter optimization. Firstly, real-time price becomes an important influencing factor of power load as people increasingly rely on demand and real-time price to adjust their electricity consumption patterns. Thus, real-time price, together with other factors that affect power load, is taken as a candidate feature, and minimal redundancy maximal relevance is applied to derive informative features from candidate features. Secondly, as for another feature, the historical load sequence, to make its selection more general, this paper employs the weighted gray relation projection algorithm for holidays to be predicted. Finally, second-order oscillation and repulsion particle swarm optimization is used for optimizing parameters of support vector machine. Moreover, the proposed model is tested via simulations on datasets of Singapore. By comparing prediction results of the proposed model, the support vector machine before improvement and other three forecasting models, this paper confirms that the improvements on support vector machine are effective, and the proposed model outperforms the other forecasting models in aspect of accuracy, stability and effectiveness.

准确的电力负荷预测有助于确保电力系统的安全调度和稳定运行。支持向量机作为一种很好的预测工具，被广泛应用于电力负荷预测中。但是，由于信息技术的飞速发展，简单的支持向量机的预测结果已经不够准确，无法在智能电网中进行预测。为了提高预测精度，本文对支持向量机进行了一些改进，并提出了一种结合智能方法进行特征选择和参数优化的混合模型。首先，随着人们越来越依赖需求和实时价格来调整其用电模式，实时价格已成为影响电力负荷的重要因素。因此，实时价格 连同影响功率负载的其他因素一起被视为候选特征，并且最小冗余最大相关性被应用于从候选特征中得出信息性特征。其次，关于历史负荷序列的另一个特征，为了使其选择更为通用，本文采用加权灰色关联投影算法对假期进行预测。最后，利用二阶振荡和排斥粒子群算法对支持向量机的参数进行优化。此外，通过对新加坡数据集的仿真对提出的模型进行了测试。通过比较所提出的模型，改进前的支持向量机和其他三种预测模型的预测结果，本文证实了对支持向量机的改进是有效的，