RS-SVM forecasting model and power supply-demand forecast |
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Authors: | Shu-xia Yang Yuan Cao Da Liu and Chen-feng Huang |
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Affiliation: | School of Economics and Management, North China Electric Power University, Beijing 102206, China |
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Abstract: | A support vector machine (SVM) forecasting model based on rough set (RS) data preprocess was proposed by combining the rough
set attribute reduction and the support vector machine regression algorithm, because there are strong complementarities between
two models. Firstly, the rough set was used to reduce the condition attributes, then to eliminate the attributes that were
redundant for the forecast, Secondly, it adopted the minimum condition attributes obtained by reduction and the corresponding
original data to re-form a new training sample, which only kept the important attributes affecting the forecast accuracy.
Finally, it studied and trained the SVM with the training samples after reduction, inputted the test samples re-formed by
the minimum condition attributes and the corresponding original data, and then got the mapping relationship model between
condition attributes and forecast variables after testing it. This model was used to forecast the power supply and demand.
The results show that the average absolute error rate of power consumption of the whole society and yearly maximum load are
14.21% and 13.23%, respectively, which indicates that the RS-SVM forecast model has a higher degree of accuracy. |
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Keywords: | rough set (RS) support vector machine (SVM) power supply and demand forecast |
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