基于气象因子Fuzzy模糊处理的短期电力负荷预测

SHORT-TERM LOAD FORECASTING WITH FUZZY PROCESSING BASED ON METEOROLOGICAL FACTORS

电力短期负荷预测受各种气象因素的影响,这导致短期电力负荷预测准确度不高。使用模糊逻辑处理温度、湿度和风速的三种影响因素,把它们转化为能被BP神经网络输入识别的具体的数据。该网络经过训练后,得到合适的权值。利用该模糊神经网络,测试电力日负荷数据,预测的平均误差约在±1.69%。

Short-term power load forecasting is impacted by various meteorological factors,which affected its forecasting accuracy.We use fuzzy logic to deal with three influencing factors：the temperature,the humidity and the wind speed,and convert them into specific data which can be inputted and recognised by BP neural network.After being trained,the fuzzy set neural network gets appropriate weight.Using the trained neural network to test daily power load data,the forecasted average errors is about ＆#177;1 .69%.