Electric Vehicle User Behavior Prediction Using Hybrid Kernel Density Estimator
| Autor: | Yu-Wei Chung, Rajit Gadh, Chi-Cheng Chu, Behnam Khaki |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Mathematical optimization
Schedule business.product_category Computer simulation Computer science 020209 energy Gaussian Kernel density estimation Scheduling (production processes) Estimator 02 engineering and technology symbols.namesake Computer Science::Systems and Control Electric vehicle 0202 electrical engineering electronic engineering information engineering symbols Hybrid kernel business |
| Zdroj: | 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). |
| DOI: | 10.1109/pmaps.2018.8440360 |
| Popis: | This paper proposes a hybrid kernel density estimator (HKDE) that uses both Gaussian- and Diffusion-based KDE (GKDE and DKDE) to predict the stay duration and charging demand of electric vehicles (EVs), which are essential parameters for optimizing EV charging schedule. While DKDE has higher accuracy in general, GKDE tends to result in better estimation for users who charge the EV irregularly. Therefore, the HKDE evaluates and categorizes the charging pattern regularity of a user, and determines which KDE to use by a novelty detection method based on the user's historical data. The estimations are then applied to an optimal EV charging algorithm to minimize load variance in an EV charging infrastructure and reduce EV charging cost. Real data is used for the numerical simulation to show the effectiveness of the proposed approach for predicting EV user behavior and scheduling EV charging load. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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