基于门控脉冲神经P系统模型的概率负荷预测

随泽远; 王军; 彭宏; 王德林; 宋戈

doi:10.11930/j.issn.1004-9649.202505075

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基于门控脉冲神经P系统模型的概率负荷预测

有源配电网安全高效运行与协同调控关键技术 | 更新时间：2026-08-04

- 基于门控脉冲神经P系统模型的概率负荷预测
- Probabilistic load prediction based on gated spiking neural P system model
- 中国电力 2026年59卷第5期页码：46-56
- 作者机构：
  
  1. 西华大学电气与电子信息学院,四川,成都,610039
  2. 西南交通大学电气工程学院,四川,成都,610031
  3. 国网四川省电力公司成都供电公司,四川,成都,610021
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目（62176216）。
- DOI：10.11930/j.issn.1004-9649.202505075
  中图分类号：
- 网络首发：2026-05-15，
  
  纸质出版：2026
- 稿件说明：
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随泽远, 王军, 彭宏, 王德林, 宋戈. 基于门控脉冲神经P系统模型的概率负荷预测[J]. 中国电力, 2026, 59(5): 46-56.

SUI Zeyuan, WANG Jun, PENG Hong, et al. Probabilistic load prediction based on gated spiking neural P system model[J]. 2026, 59(5): 46-56.
随泽远, 王军, 彭宏, 王德林, 宋戈. 基于门控脉冲神经P系统模型的概率负荷预测[J]. 中国电力, 2026, 59(5): 46-56. DOI： 10.11930/j.issn.1004-9649.202505075.

SUI Zeyuan, WANG Jun, PENG Hong, et al. Probabilistic load prediction based on gated spiking neural P system model[J]. 2026, 59(5): 46-56. DOI： 10.11930/j.issn.1004-9649.202505075.

摘要

传统的确定性负荷预测无法提供负载的不确定性信息，概率负荷预测能够生成预测值不确定性的概率分布，为电网调度决策提供更丰富的信息。为了进一步提高概率负荷预测的精度，提出了一种包含最小绝对收缩和选择算子（least absolute shrinkage and selection operator，LASSO）及门控脉冲神经P系统（gated spiking neural P system，GSNP）的LASSO-GSNP模型。首先，运用LASSO从最低温度、最高温度、平均温度、平均湿度和降雨量等外部特征中提取关键特征；随后，提出了改进的GSNP模型实现概率负荷预测，以提升长时间序列预测的性能。使用2个不同尺度的长时间序列数据集作为算例，结果表明，所提模型在预测精度指标和预测区间质量上均优于其他几种典型模型。

Abstract

Conventional deterministic load forecasting fails to provide uncertainty information of loads

while probabilistic load forecasting can generate probability distributions of predicted value uncertainty

thus providing comprehensive information for power grid dispatch decisions. In order to further improve the accuracy of probabilistic load forecasting

this paper proposes a model

which incorporates the least absolute shrinkage and selection operator (LASSO) and gated spiking neural P system (GSNP). Firstly

LASSO is used to extract the key features from external features such as minimum temperature

maximum temperature

average temperature

average humidity and precipitation. Subsequently

an improved GSNP model is developed to implement probabilistic load forecasting

enhancing the performance of long-term time-series forecasting. The case study using two long-term time-series datasets at different scales shows that the proposed model outperforms several other typical models in terms of both prediction accuracy and prediction interval quality.

关键词

Keywords

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