自动化学报2026，Vol.52Issue(6)：1145-1156,12.DOI:10.16383/j.aas.c250480
基于元认知二型模糊神经网络的电力负荷区间预测方法
Interval Prediction Method of Power Load Based on Metacognitive Type-2 Fuzzy Neural Network
摘要
Abstract
The prediction of the key indicators is a challenging problem due to the high nonlinearity and strong un-certainties in power load data.To solve this problem,a metacognitive type-2 fuzzy neural network(MCT2FNN)-based interval prediction method is proposed.First,a type-2 fuzzy rule based on multi-value mapping is designed to extend rule consequents from scalar values to interval vectors by using interval estimation technology.It can handle the variable correlation bias caused by uncertainty and capture the nonlinear relationship between the variables in the load series.Second,a type-2 fuzzy neural network(T2FNN)with an error compensation mechanism is estab-lished.In this network,a dynamic feedback structure is introduced to perceive and compensate for cumulative er-rors and model biases in real time,which can achieve high precision prediction of key indicators.Then,an interval coverage probability and interval width-based metacognitive learning algorithm is designed to adaptively optimize the boundary estimates of T2FNN through the real-time assessment of interval reliability,which can improve the confidence level of interval predictions.Finally,the proposed MCT2FNN is applied to interval prediction tasks for the urban power system.The experimental results demonstrate that the method can provide high-confidence and precise prediction intervals for power systems.关键词
元认知二型模糊神经网络/区间预测/区间覆盖率/区间宽度Key words
metacognitive type-2 fuzzy neural network/interval prediction/interval coverage probability/interval width引用本文复制引用
孙晨暄,韩红桂,伍小龙,房方..基于元认知二型模糊神经网络的电力负荷区间预测方法[J].自动化学报,2026,52(6):1145-1156,12.基金项目
一流学科人才培育计划(XM2512302)资助 Supported by First Class Discipline Talent Cultivation Pro-gram(XM2512302) （XM2512302）
