基于RW-SSA-GRNN的短期电力负荷预测

闫秀英; 樊晟志

doi:10.16513/j.2096-2185.DE.2207605

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分布式能源 ›› 2022, Vol. 7 ›› Issue (6) : 37-43. DOI: 10.16513/j.2096-2185.DE.2207605

学术研究

基于RW-SSA-GRNN的短期电力负荷预测

闫秀英 ,
樊晟志

作者信息 +

Short-Term Power Load Forecasting Based on RW-SSA-GRNN

Author information +

文章历史 +

摘要

智能电网技术的迅速发展，对短期电力负荷预测的速度、精度和稳定性都提出了更高的要求。针对智能用电环境下负荷随机性强、数据量较少情况下短期电力负荷预测精度差、计算时间长等问题，提出一种基于随机游走(random walk，RW)、改进麻雀搜索算法(sparrow search algorithm，SSA)优化广义回归神经网络(general regression neural network，GRNN)的组合预测方法。模型采用多输入单输出，输入为负荷数据和气象信息等，输出为负荷预测值。通过引入随机游走对麻雀所处位置进行扰动，避免陷入局部最优的同时进一步提高其全局搜索能力，利用改进后的麻雀搜索算法优化广义回归神经网络的平滑因子，提升模型的自学能力、稳定性和精度。以陕西省西安市某支线的实际负荷数据进行预测验证，结果表明，改进后的算法拥有更好的收敛能力，模型预测精度更高。

Abstract

With the rapid development of smart grid technology, the speed, accuracy and stability of short-term power load forecasting are required. Aiming at the problems such as strong randomness of load in intelligent electricity environment, poor accuracy of short-term power load prediction and long calculation time when the amount of data is small, a combined prediction method based on random walk (RW) and improved sparrow search algorithm (SSA) to optimize generalized regression neural network (GRNN) was proposed. The model uses multiple inputs and single outputs. The input is load data and meteorological information, and the output is the predicted hourly load values. The RW was introduced to disturb the location of the sparrow, avoid falling into the local optimal value, and further improve its global search ability. The improved SSA was used to optimize the smoothing factor of the GRNN, and improve the self-learning ability, stability and accuracy of the model. The actual load data of a branch line in Xi'an, Shaanxi province is used for forecasting and verification. The results show that the improved algorithm has better convergence ability, and the model has better prediction accuracy.

导出引用

闫秀英, 樊晟志. 基于RW-SSA-GRNN的短期电力负荷预测[J]. 分布式能源. 2022, 7(6): 37-43 https://doi.org/10.16513/j.2096-2185.DE.2207605

Xiuying YAN, Shengzhi FAN. Short-Term Power Load Forecasting Based on RW-SSA-GRNN[J]. Distributed Energy Resources. 2022, 7(6): 37-43 https://doi.org/10.16513/j.2096-2185.DE.2207605

中图分类号： TK01;TM71

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