基于人工智能的智能电网最佳负荷预测策略(OLFS)

A. H. Rabie, Ahmed I. Saleh, Said H. Abd Elkhalik, Ali E. Takieldeen
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摘要

最近,人工智能(AI)在许多生活领域的应用提高了系统的效率,并将其转化为智能系统,尤其是在能源领域。将人工智能与电力系统相结合,可以使电网变得足够智能,从而预测未来的负荷,这就是所谓的智能负荷预测(ILF)。因此,可以相应地为电力系统规划和运行程序做出适当的决策。此外,ILF 还能在电力需求响应中发挥重要作用,从而保证电力系统的可靠过渡。本文介绍了一种基于人工智能技术的最佳负荷预测策略(OLFS),用于预测智能电网的未来负荷。所提出的 OLFS 包括两个连续阶段,分别是数据预处理阶段(DPP)和负荷预测阶段(LFP)。在前一个阶段,在进行实际预测之前,先要准备好输入的电力负荷数据集,然后再进行两项基本任务,即特征选择和异常值剔除。特征选择是通过高级豹印优化(ALSO)这一全新的自然启发优化技术来实现的,而离群值剔除则是通过四分位数间距(IQR)这一统计离散度量来完成的。另一方面,在 LFP 中使用一种名为加权 K 近邻(WKNN)算法的新预测器进行实际负荷预测。拟议的 OLFS 已通过大量实验进行了测试。结果表明,OLFS 的性能优于最新的负荷预测技术,因为它能以最小的均方根误差实现最高的预测精度。
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An Optimum Load Forecasting Strategy (OLFS) for Smart Grids Based on Artificial Intelligence
Recently, the application of Artificial Intelligence (AI) in many areas of life has allowed raising the efficiency of systems and converting them into smart ones, especially in the field of energy. Integrating AI with power systems allows electrical grids to be smart enough to predict the future load, which is known as Intelligent Load Forecasting (ILF). Hence, suitable decisions for power system planning and operation procedures can be taken accordingly. Moreover, ILF can play a vital role in electrical demand response, which guarantees a reliable transitioning of power systems. This paper introduces an Optimum Load Forecasting Strategy (OLFS) for predicting future load in smart electrical grids based on AI techniques. The proposed OLFS consists of two sequential phases, which are: Data Preprocessing Phase (DPP) and Load Forecasting Phase (LFP). In the former phase, an input electrical load dataset is prepared before the actual forecasting takes place through two essential tasks, namely feature selection and outlier rejection. Feature selection is carried out using Advanced Leopard Seal Optimization (ALSO) as a new nature-inspired optimization technique, while outlier rejection is accomplished through the Interquartile Range (IQR) as a measure of statistical dispersion. On the other hand, actual load forecasting takes place in LFP using a new predictor called the Weighted K-Nearest Neighbor (WKNN) algorithm. The proposed OLFS has been tested through extensive experiments. Results have shown that OLFS outperforms recent load forecasting techniques as it introduces the maximum prediction accuracy with the minimum root mean square error.
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