中国电力2026，Vol.59Issue(5)：46-56,11.DOI:10.11930/j.issn.1004-9649.202505075
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
摘要
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
概率负荷预测/最小绝对收缩和选择算子/深度神经网络/分位数回归/门控脉冲神经P系统Key words
probabilistic load forecasting/least absolute shrinkage and selection operator/deep neural network/quantile regression/gated spiking neural P system引用本文复制引用
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):46-56,11.基金项目
This work is supported by the National Natural Science Foundation of China(No.62176216). 国家自然科学基金资助项目(62176216). （No.62176216）
