Short Term Load Forecasting Based on PCA and LS-SVM
| Autor: | Hui Yu, Jian Dong Jiang, Zhen Zhu Wei, Ren Ran Wei, Rong Rong, Yi Wang |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
business.industry
Computer science Load forecasting General Engineering Pattern recognition computer.software_genre Term (time) Support vector machine Dimension (vector space) Principal component analysis Least squares support vector machine Feature (machine learning) Data mining Artificial intelligence business computer |
| Zdroj: | Advanced Materials Research. :4193-4197 |
| ISSN: | 1662-8985 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|