摘要
为提高预测精度,提出一种基于负荷分解的电力系统短期负荷预测方法。即将负荷分成周期性不同的几部分,对分解后的各负荷序列通过相匹配的神经网络方法进行预测,并考虑温度因素的影响,采用线性回归模型对神经网络预测结果修正得到最终预测结果,预测结果与实际数据对比得出,预测方法更具准确性。
For improving the precision of short term load forecasting,this paper advances a power system short load forecasting method on load decomposition,it means that the load is decomposed into several parts on account of different periods,then we use matching artificial neural network to forecast every part,then takes the influence of temperature into account and uses linear regression model to modify the result that artificial neural network forecast. Comparing with the predicted and actual data,the result verifies the accuracy of this forecasting model.
出处
《河北电力技术》
2010年第2期11-14,共4页
Hebei Electric Power
关键词
短期负荷预测
小波分解
人工神经网络
温度
short term load forecasting
wavelet decomposition
artificial neural network
temperature
