Since load forecasting plays a decisive role in the safe and stable operation of power systems, it is particularly important to explore forecasting methods accurately. In this article, the hybrid empirical mode decomposition (EMD) and support vector regression (SVR) with back‐propagation neural network (BPNN), namely the EMDHR‐SVR‐BPNN model, is proposed. Information theory is mainly used to solve the data tendency problem, and the EMD method is used to solve the data volatility problem. There is no interaction between these two methods; thus these two models can complement each other through generalized regression of orthogonal decomposition. Taking the load data from the New South Wales (NSW, Australia) market as an example, the obtained simulation results are compared with other models. It is concluded that the proposed EMDHR‐SVR‐BPNN model not only improves the forecasting accuracy but also has good fitting ability. It can reflect the changing tendency of data in a timely manner, providing a strong basis for the electricity generation of the power sector in the future, thus reducing electricity waste. The proposed EMDHR‐SVR‐BPNN model has potential for employment in mid‐short term load forecasting.

中文翻译：

---

基于混合经验模式分解和支持向量回归的反向传播神经网络的中短期负荷预测的广义回归模型

由于负荷预测在电力系统的安全稳定运行中起着决定性的作用，因此准确探索预测方法尤为重要。在本文中，提出了具有反向传播神经网络（BPNN）的混合经验模式分解（EMD）和支持向量回归（SVR），即EMDHR-SVR-BPNN模型。信息论主要用于解决数据趋势问题，EMD方法用于解决数据波动性问题。这两种方法之间没有交互作用。因此，这两个模型可以通过正交分解的广义回归相互补充。以来自新南威尔士州（澳大利亚NSW）市场的负荷数据为例，将获得的仿真结果与其他模型进行比较。结论表明，提出的EMDHR-SVR-BPNN模型不仅提高了预测精度，而且具有良好的拟合能力。它可以及时反映数据的变化趋势，为将来电力部门的发电提供坚实的基础，从而减少电力浪费。拟议的EMDHR-SVR-BPNN模型具有在中短期负荷预测中使用的潜力。