An Ensemble Feature Selection Method for Short-Term Electrical Load Forecasting
| Autor: | Yanhui Zou, Guorui Wang, Ting Li, Junxingxu Chen, Huisheng Ye, Fengyi Lv |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Electrical load
Computer science 020209 energy Feature selection 02 engineering and technology computer.software_genre Term (time) Weighting Feature (computer vision) 0202 electrical engineering electronic engineering information engineering Key (cryptography) 020201 artificial intelligence & image processing Data mining Electric power computer Selection (genetic algorithm) |
| Zdroj: | 2019 IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2). |
| DOI: | 10.1109/ei247390.2019.9062042 |
| Popis: | The fluctuation of electrical power load is affected by numerous external factors, such as climate change, economic development, social form and special events etc. The optimal feature subset selection is the key to improving the accuracy of short-term power load forecasting. The traditional feature selection methods have encountered difficulties in balancing performance and computational consumption. The development of ensemble feature selection methods is a feasible way to solve this dilemma. In this paper, an embedded ensemble feature selection method is established and investigated on a short-term electrical load forecasting task. A new weighting approach is proposed to combine the result of individual embedded feature selection methods. The results of the embedded ensemble feature selection method show a significant increase in accuracy compared to the individual feature selection methods. |
| Databáze: | OpenAIRE |
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