Power system load forecasting by improved principal component analysis and neural network
Power system load forecasting by improved principal component analysis and neural network
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DOI：
10.1109/ichve.2016.7800613
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发表时间：
2016-09
期刊：
影响因子：
--
通讯作者：
Xiao-fei Liu;Li-qun Shang
中科院分区：
文献类型：
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作者：
Xiao-fei Liu;Li-qun Shang
电力系统负荷预测是电力系统规划和运行的基础，为了提高电力系统负荷预测的准确性，保证电力系统的稳定运行，在电力系统负荷预测的研究中必须综合考虑多项相关指标。传统的主成分分析方法通常被用来处理这些指标。针对传统主成分分析法所保留的信息不全面的问题。采用改进的主成分分析法对初始数据进行均值化处理，有效地消除了改进后的评价指标维数和数量级的影响，更全面地反映了初始数据中所包含的各指标的变异程度信息和不同指标之间的相互影响，降低了评价指标的维数，通过对电力系统负荷预测主成分的提取，可以有效地减少径向基函数网络的输入，提高电力系统负荷预测的精度。实例分析结果验证了该方法的有效性。
Power system load forecasting is the basis of power system planning and operation, in order to improve the accuracy of power system load forecasting and ensure the stable operation of power system, a number of related indices must be taken into consideration in the research of power system load forecasting. The tradition principal component analysis is always used to process these indices. In allusion to the problem that information reserved by tradition principal component analysis is not comprehensive. The improved principal component analysis is used to mean processing initial data, it can effectively eliminate the improved influence of the evaluation indices dimension and order of magnitude, reflecting information on the degree of variation of each index and the mutual influence between different indicators contained in the initial data more comprehensively, reducing the evaluation indices dimension, acquiring the principal components of power system load forecasting, it can effectively reduce the input of the radial basis function network and improve the accuracy of load forecasting of power system. The effectiveness of the proposed method is validated by results of case analysis.