Electric Vehicle Driver Clustering using Statistical Model and Machine Learning
| Autor: | Rajit Gadh, Yingqi Xiong, Chi-Cheng Chu, Bin Wang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
business.product_category Energy management business.industry Computer science 020209 energy 020208 electrical & electronic engineering Statistical model 02 engineering and technology Machine learning computer.software_genre Scheduling (computing) Machine Learning (cs.LG) Computer Science - Learning Electric vehicle 0202 electrical engineering electronic engineering information engineering Artificial intelligence business Cluster analysis computer |
| DOI: | 10.48550/arxiv.1802.04193 |
| Popis: | Electric Vehicle (EV) is playing a significant role in the distribution energy management systems since the power consumption level of the EVs is much higher than the other regular home appliances. The randomness of the EV driver behaviors make the optimal charging or discharging scheduling even more difficult due to the uncertain charging session parameters. To minimize the impact of behavioral uncertainties, it is critical to develop effective methods to predict EV load for smart EV energy management. Using the EV smart charging infrastructures on UCLA campus and city of Santa Monica as testbeds, we have collected real-world datasets of EV charging behaviors, based on which we proposed an EV user modeling technique which combines statistical analysis and machine learning approaches. Specifically, unsupervised clustering algorithm, and multilayer perceptron are applied to historical charging record to make the day-ahead EV parking and load prediction. Experimental results with cross-validation show that our model can achieve good performance for charging control scheduling and online EV load forecasting. Comment: 2018 IEEE PES General Meeting |
| Databáze: | OpenAIRE |
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