基于大数据降维及优化神经网络的负荷预测研究

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中图分类号：TP391.92;TM711 文献标志码：A 文章编号：1001-5922（2025）05-0182-04

Abstract：In order to improve the powerload prediction ability，a setof power loaddata intelligent monitoring system was designed，and itshardwarestructureincluded information processing terminal，network communicationand monitoring model，etc.，and the system could significantlyimprove the operationcapacityof theentire transmission line.In this study，an embedded monitoring system with the main control chip as the TMS32ODM8168 was designed，and the data computing capability was improved by improving the BP neural network model. Based on the XGboost fusion model，abnormal data information monitoring was realized.Through experiments on the 35kV power load data path，it wasfound thatthe number of designed monitoring lineswas 36，the monitoring claritywas ，and the algorithm recognition accuracy was 97.3% ，which greatly improves the monitoring ability. Key words ：power load； monitoring；embedded monitoring；BP neural network model；XGboost fusion model

随着输电线路的增加，电力负荷数据的整体规划和智能监控越发重要。（剩余5040字）