Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach

TL;DR

This paper introduces a reinforcement learning assisted deep learning framework that effectively captures the uncertainties in EV charging power forecasting, improving prediction accuracy for large-scale transportation electrification.

Contribution

It proposes a novel combination of LSTM and reinforcement learning with an adaptive exploration PPO algorithm for probabilistic EVCS charging power forecasting.

Findings

The framework outperforms existing methods in accuracy.

It effectively models forecast uncertainties.

Experimental results validate its robustness on real data.

Abstract

The electric vehicle (EV) and electric vehicle charging station (EVCS) have been widely deployed with the development of large-scale transportation electrifications. However, since charging behaviors of EVs show large uncertainties, the forecasting of EVCS charging power is non-trivial. This paper tackles this issue by proposing a reinforcement learning assisted deep learning framework for the probabilistic EVCS charging power forecasting to capture its uncertainties. Since the EVCS charging power data are not standard time-series data like electricity load, they are first converted to the time-series format. On this basis, one of the most popular deep learning models, the long short-term memory (LSTM) is used and trained to obtain the point forecast of EVCS charging power. To further capture the forecast uncertainty, a Markov decision process (MDP) is employed to model the change of LSTM cell states, which is solved by our proposed adaptive exploration proximal policy optimization (AePPO) algorithm based on reinforcement learning. Finally, experiments are carried out on the real EVCSs charging data from Caltech, and Jet Propulsion Laboratory, USA, respectively. The results and comparative analysis verify the effectiveness and outperformance of our proposed framework.