Forecasting of EVs Charging Behavior Using Deep Neural Networks

As a source of new-generation transportation, Electric Vehicles (EVs) are becoming popular daily, as climate change is a severe issue in which global energy consumption is the primary factor that causes a climatic crisis. The mobility sector accounts for twenty-five percent of Total energy consumption. To resolve this, EVs are becoming more and more popular as they can reduce greenhouse gas emissions to a large extent. A few challenge that come with the EVs deployment is that it causes strain on the Power Grid. The solution to this problem can be found by using smart scheduling algorithms, Data-driven techniques, machine learning (ML), and Deep Neural Networks (DNN) to manage the growing public charging demand. To recognize the user charging pattern which predicts analytic capability, the proposed work uses the DNN model to forecast EV energy demand. And here, we have compared the combination of DNN and ML models and found the LSTM and random forest to perform better than other techniques.