Computer Science ›› 2018, Vol. 45 ›› Issue (7): 315-321.doi: 10.11896/j.issn.1002-137X.2018.07.053

• Interdiscipline & Frontier • Previous Articles    

Short-term Load Forecasting Method Combining Multi-scale Analysis with Data Co-transfer

LIU Shi-chang, JIN Min   

  1. College of Computer Science and Electronic Engineering,Hunan University,Changsha 410006,China
  • Received:2017-05-18 Online:2018-07-30 Published:2018-07-30

Abstract: In order to improve the performance of short-term load forecasting,this paper proposed a short-term load forecasting method combining multi-scale analysis with data co-transfer.On the one hand,aiming at the problem that the hidden information in the original series which is related to the subseries isn’t fully utilized in the modeling and prediction of subseries in multi-scale analysis forecasting method,this paper used mutual information feature selection method to select appropriate past loads and introduced them into the feature set of the approximation component of original load series.By expanding feature set,more information can be provided for learning algorithm,which can further improve the forecasting accuracy of the approximate component.On the other hand,aiming at the problem thatdifferent kinds of data can influence model’s performance,this paper proposed a transfer learning method based on kernel ridge regression to transfer similar data to data corresponding to days to be forecasted.By doing this,the similarity of these data was used and the difference of these data was taken into account during modeling.Case study shows that the proposed me-thod outperforms in MAPE,MAE and RMSE which are decreased by 6.2%,3.4% and 5.5% respectivelywhen compared with single model forecasting method.

Key words: Data co-transfer, Feature-expanding, Kernel ridge regression, Multi-scale analysis, Short-term load forecasting

CLC Number: 

  • TM734
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