Short Term Load Forecasting Based on PCA and LS-SVM

Ren Ran  Wei; Zhen Zhu  Wei; Rong  Rong; Yi  Wang; Jian Dong  Jiang; Hui  Yu

doi:10.4028/www.scientific.net/AMR.756-759.4193

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 756-759Short Term Load Forecasting Based on PCA and...

Short Term Load Forecasting Based on PCA and LS-SVM

Abstract:

In this paper, in order to improve the precision of the short-term load forecasting, we propose a power load forecasting method combined principal component analysis (PCA) with least squares support vector machine (LS-SVM). Firstly PCA extracts the feature of the influence factors for power load, and then LS-SVM constructs a training model with a new variables extracted by PCA. After using PCA-LS-SVM model this paper proposed to forecast power load of one area, the results show that this method can effectively eliminate the redundant information among influential factors, reduce the input dimension of the prediction model, simplify the structure of the network, increase the learning speed and improve the power load forecasting accuracy. So this method is effectively feasible.

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Periodical:

Advanced Materials Research (Volumes 756-759)

Pages:

4193-4197

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.756-759.4193

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Online since:

September 2013

Authors:

Ren Ran Wei, Zhen Zhu Wei, Rong Rong, Yi Wang, Jian Dong Jiang, Hui Yu

Keywords:

Least Squares Support Vector Machines (Ls-SVM), Principal Component Analysis (PCA), Short Term Load Forecasting

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