一种电力系统短期负荷预测的新方法

A New Method of Electric Power System Short-term Load Forecasting

本文把一种新的灰色理论自修正模型应用到负荷预测的误差校正中。提出了纵向负荷预测与横向误差校正相结合的方法。首先选用同一时刻并且具有相同影响因素的负荷作为人工神经网络的输入进行纵向负荷预测 ,利用预测时刻之前的整点时刻负荷预测的误差来建立一个带有自修正功能的灰色理论模型进行负荷预测的横向误差校正。这种方法提高了预测的精度。

A new gray theory self-correcting model is applied to the error correct of Short-term Load Forecasting Technique. This paper givesa forward the associative method of lengthways load forecasting and transverse error correcting. First, we forecasts the load by choosing the loads with same affecting factor and same time for the input of artificial nerve network, then transversely corrects the error by using the sharp-time forecasting error before forecasting time to set up a gray-theory model with self-correcting function. The method improved exactness of forecasting.