Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods
| Autor: | Fares Aljuheshi, Mahmoud Alahmad, Mostafa Rafaie, Ahmad Almaghrebi, Kevin James |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Control and Optimization
business.product_category Computer science Reliability (computer networking) charging demand Stability (learning theory) Energy Engineering and Power Technology Machine learning computer.software_genre lcsh:Technology Session (web analytics) Scheduling (computing) Hardware_GENERAL Electric vehicle public charging stations Electrical and Electronic Engineering Engineering (miscellaneous) Plug-in Electric Vehicle charging behavior machine learning data-driven Renewable Energy Sustainability and the Environment business.industry lcsh:T Energy consumption Grid Smart grid Artificial intelligence business computer Energy (miscellaneous) |
| Zdroj: | Energies; Volume 13; Issue 16; Pages: 4231 Energies, Vol 13, Iss 4231, p 4231 (2020) |
| ISSN: | 1996-1073 |
| DOI: | 10.3390/en13164231 |
| Popis: | Plug-in Electric Vehicle (PEV) user charging behavior has a significant influence on a distribution network and its reliability. Generally, monitoring energy consumption has become one of the most important factors in green and micro grids; therefore, predicting the charging demand of PEVs (the energy consumed during the charging session) could help to efficiently manage the electric grid. Consequently, three machine learning methods are applied in this research to predict the charging demand for the PEV user after a charging session starts. This approach is validated using a dataset consisting of seven years of charging events collected from public charging stations in the state of Nebraska, USA. The results show that the regression method, XGBoost, slightly outperforms the other methods in predicting the charging demand, with an RMSE equal to 6.68 kWh and R2 equal to 51.9%. The relative importance of input variables is also discussed, showing that the user’s historical average demand has the most predictive value. Accurate prediction of session charging demand, as opposed to the daily or hourly demand of multiple users, has many possible applications for utility companies and charging networks, including scheduling, grid stability, and smart grid integration. |
| Databáze: | OpenAIRE |
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