The Application of Improved Random Forest Algorithm on the Prediction of Electric Vehicle Charging Load
| Autor: | Chen Huafeng, Xiancheng Zhong, Bao Xianlu, Wei Enwei, Yiqi Lu, Yongpan Li, Da Xie |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Control and Optimization
business.product_category Computer science 020209 energy Load forecasting data analysis load forecasting Real-time computing Energy Engineering and Power Technology 02 engineering and technology lcsh:Technology Charging station electric vehicle (EV) random forest charging load Electric vehicle 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Engineering (miscellaneous) lcsh:T Renewable Energy Sustainability and the Environment 020208 electrical & electronic engineering Power (physics) Random forest business Energy (miscellaneous) |
| Zdroj: | Energies; Volume 11; Issue 11; Pages: 3207 Energies, Vol 11, Iss 11, p 3207 (2018) |
| ISSN: | 1996-1073 |
| DOI: | 10.3390/en11113207 |
| Popis: | To cope with the increasing charging demand of electric vehicle (EV), this paper presents a forecasting method of EV charging load based on random forest algorithm (RF) and the load data of a single charging station. This method is completed by the classification and regression tree (CART) algorithm to realize short-term forecast for the station. At the same time, the prediction algorithm of the daily charging capacity of charging stations with different scales and locations is proposed. By combining the regression and classification algorithms, the effective learning of a large amount of historical charging data is completed. The characteristic data is divided from different aspects, realizing the establishment of RF and the effective prediction of fluctuate charging load. By analyzing the data of each charging station in Shenzhen from the aspect of time and space, the algorithm is put into practice. The application form of current data in the algorithm is determined, and the accuracy of the prediction algorithm is verified to be reliable and practical. It can provide a reference for both power suppliers and users through the prediction of charging load. |
| Databáze: | OpenAIRE |
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