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Middle-term power load forecasting model based on kernel principalcomponent analysis and improved neural network(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2018年03期
Page:
259-
Research Field:
Publishing date:

Info

Title:
Middle-term power load forecasting model based on kernel principalcomponent analysis and improved neural network
Author(s):
Sun XinchengKong JianshouLiu Zhao
School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China
Keywords:
kernel principal component analysis particle swarm optimization back propagation neural network power load middle-term forecasting
PACS:
TM715
DOI:
10.14177/j.cnki.32-1397n.2018.42.03.001
Abstract:
A forecasting model is proposed by combing kernel principal component analysis(KPCA)with particle swarm optimization and back propagation neural network(PSO-BPNN)to improve the level of middle-term power load forecasting. Dimensionality reduction and reconstruction of the original input space are made with the KPCA. The data set after dimensionality reduction is input to a BPNN model optimized by PSO. The average daily peak load in each month is forecasted to revise the daily load,and the daily peak forecasting load is output in the end. This model is tested using the data provided by the European Network on Intelligent Technologies(EUNITE),and the mean absolute percent error(MAPE)of this model is 1.39%.

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Last Update: 2018-06-30