Based on Partial Least Squares Linear Regression and the Improved Grey Prediction Model of Monthly Load Forecasting in Power System Applications

Zhi Jian Liu; Zhi Hua Yang; Rong Chen; Shu Ming Zhou

doi:10.4028/www.scientific.net/AMR.827.428

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 827Based on Partial Least Squares Linear Regression...

Based on Partial Least Squares Linear Regression and the Improved Grey Prediction Model of Monthly Load Forecasting in Power System Applications

Abstract:

Aiming at monthly load of power system, it is forecasted by using the method of partial least squares regression and the model of improving grey prediction.First, using improved grey prediction model to forecast impact factors,then establishing partial least squares model according to the characteristics of the monthly load and the change of the main impact factors. The final fitted out a linear relation between load and impact factors. Practical example shows that the method has higher prediction accuracy, effectiveandfeasible.

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Periodical:

Advanced Materials Research (Volume 827)

Pages:

428-434

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.827.428

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Online since:

October 2013

Authors:

Zhi Jian Liu, Zhi Hua Yang, Rong Chen, Shu Ming Zhou

Keywords:

Impact Factors, Improved Grey Model, Load Forecast, Partial Least Squares Fitted

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