基于气象大数据的配网故障数量预测

Quantity Prediction of Distribution Network Faults Based on Big Meteorological Data

将配网故障抢修历史数据与庞大的温度、降雨、雷电等气象数据相结合,建立了一种具有天气敏感性的神经网络组合预测模型.该模型利用最大相关系数实时调整导致抢修数量变化的主要气象因素,精确预测故障发生的数量、类型和变化趋势,为抢修人员和物资的提前部署提供定性、定量指导.

With historical data of distribution network faults and big data of temperature,rainfall,and lighting,a weather-sensitive neural network predicting modelis proposed.Based on the maximum correlation coefficient of faults and meteorological factors,the main meteorological factors are real-time adjusted.The number,type and trend of the faults are accurately predicted.The result provides qualitative and quantitative reference for staff and materials deployment.