基于功率谱分解和实时气象因素的短期负荷预测Short-Term Load Forecasting Based on Power Spectrum Decomposition and Hourly Weather Factors
张凯;姚建刚;李伟;贺辉;
摘要(Abstract):
提出了基于功率谱分解和实时气象因素的短期负荷预测方法,采用快速傅里叶变换(fast Fourier transform,FFT)对负荷序列进行变换得到功率谱,依据变换结果分析功率谱得出负荷基频、低频和高频分量的频率范围,采用有限脉冲响应(finite impulse response,FIR)滤波器从负荷中分离出各个负荷分量。分析各个负荷分量的特点,针对各个负荷分量分别设计预测模型,对基频分量采用Elman回归神经网络进行预测,这部分较好地反映出基频分量的时间序列特性;对低频和高频分量分别采用自适应线性回归神经网络进行预测,在对这部分分量的预测中重点引入实时气象因素,以利用最新的气象信息提高预测精度。通过在某地区的实际应用证明了所提出方法的有效性。
关键词(KeyWords): 谱分解;实时气象因素;短期负荷预测;人工神经网络;电力系统
基金项目(Foundation):
作者(Author): 张凯;姚建刚;李伟;贺辉;
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DOI:
参考文献(References):
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