Abstract Electric power systems are moving toward smarter and more sustainable systems. These trends result in several positive advantages such as active participation of customers in electricity markets. However, resulting demand side flexibilities cause high demand fluctuations and increase the difficulty to maintain the power balance and reliability of smart grids. To address this challenge, this paper proposes a self-partitioning local neuro fuzzy model, which is capable of performing a fast and accurate short-term load forecasting. The proposed model, not only maintains the linearity as well as learning–from-data property via their fuzzy inference systems of local linear neuro fuzzy, but also benefits from partitioning the input space into linear and nonlinear vectors and assigning them separately into different local models. The proposed model is trained with the hierarchical binary-tree learning algorithm and rule premises are calculated through sigmoid partitioning functions. These appealing properties make the model appropriate for a fast and accurate analysis of the load time series featuring both linear and nonlinear characteristics. The effectiveness of the proposed model is compared with recently published forecasting models in terms of statistical performance.

中文翻译：

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用于智能电网短期负荷预测的自划分局部神经模糊模型

摘要 电力系统正朝着更智能、更可持续的系统发展。这些趋势带来了一些积极的优势，例如客户积极参与电力市场。然而，由此产生的需求侧灵活性导致需求波动较大，增加了维持智能电网功率平衡和可靠性的难度。为了应对这一挑战，本文提出了一种自分区局部神经模糊模型，该模型能够执行快速准确的短期负载预测。所提出的模型不仅通过其局部线性神经模糊的模糊推理系统保持了线性和从数据中学习的特性，而且受益于将输入空间划分为线性和非线性向量并将它们分别分配到不同的局部模型中. 所提出的模型使用分层二叉树学习算法进行训练，并通过 sigmoid 分区函数计算规则前提。这些吸引人的特性使该模型适用于对具有线性和非线性特征的负载时间序列进行快速准确的分析。在统计性能方面，将所提出模型的有效性与最近发布的预测模型进行了比较。