Short-term load forecasting has been playing an increasingly important role in electric power systems. Effective forecasting of the future electricity demand, however, is difficult in view of the complicated effects on load by a variety of factors. A hybrid method based support vector regression (SVR) with meteorological factors and electricity price is proposed to address such problem. First, the input used in this paper, are specific ratio value combinations of each characteristic parameter affecting the hourly electricity load. Second, SVR is used to analyze and develop a load forecasting model. Third, an improved adaptive genetic algorithm (IAGA) is utilized to optimize the specific ratio value combinations of each characteristic parameter, penalty factor C and Gaussian kernel function σ to accurately establish a forecasting model. The experimental results show that the proposed method can obtain better forecasting performance in comparison with other standard and state-of-the-art methods.

中文翻译：

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一种每小时电力需求预测的新方法

短期负荷预测在电力系统中发挥着越来越重要的作用。然而，考虑到多种因素对负荷的复杂影响，对未来电力需求的有效预测是困难的。提出了一种基于支持向量回归（SVR）与气象因素和电价的混合方法来解决该问题。首先，本文中使用的输入是影响每小时电力负荷的每个特征参数的特定比率值组合。其次，SVR 用于分析和开发负荷预测模型。第三，利用改进的自适应遗传算法（IAGA）优化各特征参数、惩罚因子C和高斯核函数σ的具体比值组合，准确建立预测模型。