Application of Forecasting Power Based on Neural Network and Wilcoxon Rank Sum Test

Abstract:

With the development of modern power systems, the accuracy requirement for load forecasting is getting higher and higher. In this paper, the Wilcoxon rank sum test as a new mathematical method to test error, is applied to the neural network based short-term load forecasting model. The actual historical load data and the associated weather conditions factors to be considered, based on MATLAB neural network toolbox, The method to construct three different neural network model, the power load in Yichang area in 2009 was forecasted and the simulation of short-term. The simulation results show：Wilcoxon rank sum test algorithms in neural network model, not only can correctly predict，relative to the average relative error, in the statistical methods and statistical laws - especially in the face of large amounts of data - through a random sample to show a certain advantage in the overall distribution.

• Periodical: Advanced Materials Research (Volume 1046)
• Pages: 560-563
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1046.560
• Online since: October 2014
• Authors: Zhi Peng Tian*, Chang Hao Xia, Jian Ping Chen
• Keywords: Load Forecasting, Neural Network (NN), Wilcoxon Rank Sum Test

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