计算机工程与应用2025，Vol.61Issue(4)：368-376,9.DOI:10.3778/j.issn.1002-8331.2309-0462
自适应迭代学习的调度自动化系统运行指标预测方法
Adaptive Iterative Learning for Predicting Metrics of Dispatching Automation System
摘要
Abstract
A adaptive iterative learning method for predicting metrics of dispatching automation systems is proposed to address issues such as low accuracy of a single algorithm applied to massive metrics and failure to iteratively update based on real-time data feature changes in the context of intelligent risk warning scenarios.Based on the temporal data characteristics of metrics under different behavioral patterns of business applications in power system,a classification method for metrics based on Fourier transform and autocorrelation coefficient is proposed.Based on the classification results,an adaptive selection strategy is adopted to construct a timeseries prediction model for the metric.Real-time metric changes are dynamically captured and adaptively iterated to update model and prediction results.This paper selects some metric data of a system for example analysis to verify that the proposed method is significantly better than a single algo-rithm in terms of accuracy and timeliness,eliminating the impact of real-time data feature changes on metric prediction.关键词
调度自动化系统/自适应选择/迭代学习/自适应更新/运行指标时序预测Key words
dispatching automation system/adaptive selection/iterative learning/adaptive updates/timeseries prediction of metrics分类
信息技术与安全科学引用本文复制引用
沈嘉灵,季学纯,高尚,王宇冬,陈子韵,李昊..自适应迭代学习的调度自动化系统运行指标预测方法[J].计算机工程与应用,2025,61(4):368-376,9.基金项目
国家电网公司总部科技项目(SGFJ0000DKJS2310434). （SGFJ0000DKJS2310434）
