A hybrid transfer learning model for short-term electric load forecasting
| Autor: | Xianze Xu, Zhaorui Meng |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Electrical load
Series (mathematics) Computer science 020209 energy Applied Mathematics 020208 electrical & electronic engineering Negative transfer Regression analysis 02 engineering and technology computer.software_genre Term (time) 0202 electrical engineering electronic engineering information engineering Data mining Electrical and Electronic Engineering Time series Transfer of learning computer Decomposition of time series |
| Zdroj: | Electrical Engineering. 102:1371-1381 |
| ISSN: | 1432-0487 0948-7921 |
| DOI: | 10.1007/s00202-020-00930-x |
| Popis: | Transfer learning approach can be applied to electric load forecasting because electric load data from nearby locations are significantly correlated. However, ordinary transfer learning methods may bring negative transfer into load forecasting as time series prediction is not exactly the same as traditional data regression problem. Consequently, this paper proposes a novel hybrid transfer learning model based on time series decomposition. Firstly, trend and seasonal components are handled by standard machine learning approach so that seasonal cycles of electric load data can be interpreted better. Secondly, two-stage transfer regression model is established to forecast the irregular component in order to improve the forecasting accuracy. The negative transfer is successfully avoided, and the prediction accuracies are significantly improved because of time series decomposing and the additional information provided by the related dataset. The case study presented by two real-world power load datasets illustrates that the proposed approach can improve electric load prediction for a location by 30% at most by using additional data from another location. |
| Databáze: | OpenAIRE |
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