The short-term power load forecast is deeply studied by integrating fuzzy BP neural networks and composite adaptive filtering in this paper. Due to the difficulty on accurate modeling of complex factors in smart grid, the fuzzy technology is introduced to deal with the uncertain factors. Meanwhile, data-driven method, such as neural networks, is used as the basic frame of the power load forecast by combing the fuzzy technology. Thereby, to design a highly effective training scheme becomes the main problem on the weights of the BP-NNs. To realize better weights training, the combined filtering technology is fully adopted by fusing adaptive filtering methods based on observable degree (OD) analysis, Sage–Husa adaptive technology, and variational Bayesian method. Thereby, a novel power load forecast algorithm is proposed based on the fuzzy BP-NNs and the combined adaptive cubature Kalman filter. Experiment based on practical power load data is presented to validate the effectiveness of the proposed short-term power load algorithm.

中文翻译：

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基于权重动态估计的模糊BP神经网络的电力负荷预测。

本文将模糊BP神经网络与复合自适应滤波相结合，对短期电力负荷预测进行了深入研究。由于智能电网中复杂因素难以准确建模，因此引入了模糊技术来处理不确定因素。同时，结合模糊技术，将神经网络等数据驱动方法作为电力负荷预测的基本框架。因此，设计高效的训练方案成为BP神经网络权重的主要问题。为了实现更好的权重训练，基于可观察度（OD）分析，Sage-Husa自适应技术和变分贝叶斯方法的融合自适应滤波方法完全采用了组合滤波技术。从而，提出了一种基于模糊BP神经网络和组合式自适应卡尔曼滤波的电力负荷预测算法。提出了基于实际电力负荷数据的实验，以验证所提出的短期电力负荷算法的有效性。