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- Title
Bagging Based Multi-Source Learning and Transfer Regression for Electricity Load Forecasting.
- Authors
Zhaorui Meng
- Abstract
Accurate electricity load forecasting is critical to power system operation. Prediction error of load forecasting can be greatly reduced by utilizing knowledge transferred from other related tasks. To further improve the effectiveness of transfer, knowledge can be transferred from multiple sources to increase the chance of finding samples closely related to the target. In this work, a multi-source instances transfer algorithm based on domain-to-domain similarity and sample to domain similarity is developed and a bagging-based re-sampling transfer regression framework is constructed. Experimental evaluation on a real-world dataset shows that forecasting performance can be significantly improved by transferring useful data from more sources. Negative transfer is avoided effectively.
- Subjects
LOAD forecasting (Electric power systems); TRANSFER of training; FORECASTING; ELECTRICITY; KNOWLEDGE transfer; LUGGAGE; ELECTRIC vehicles
- Publication
IAENG International Journal of Computer Science, 2022, Vol 49, Issue 2, p335
- ISSN
1819-656X
- Publication type
Article