Reinforcement Learning-Based Load Forecasting of Electric Vehicle Charging Station Using Q-Learning Technique

The electric vehicles' (EVs) rapid growth can potentially lead power grids to face new challenges due to load profile changes. To this end, a new method is presented to forecast the EV charging station loads with machine learning techniques. The plug-in hybrid EVs (PHEVs) charging can be categorized into three main techniques (smart, uncoordinated, and coordinated). To have a good prediction of the future PHEV loads in this article, the Q-learning technique, which is a kind of the reinforcement learning, is used for different charging scenarios. The proposed Q-learning technique improves the forecasting of the conventional artificial intelligence techniques such as the recurrent neural network and the artificial neural network. Results prove that PHEV loads can accurately be forecasted by using the Q-learning technique under three different scenarios (smart, uncoordinated, and coordinated). The simulations of three different scenarios are obtained in the Keras open source software to validate the effectiveness and advantages of the proposed Q-learning technique.

Automated summary

This article introduces a method to forecast EV charging station loads using Q-learning technique, which improves the predictive capabilities of traditional artificial intelligence techniques. Results demonstrate that the Q-learning technique can accurately forecast PHEV loads under different scenarios.