Energy cost minimisation to support electric vehicle charging using machine learning

Abstract

The rapid growth of EVs and the integration of renewable energy has made the administration of charging demand and reduction of electric expenses more complex. This paper introduces an dual machine learning forecasting of EV charging demand, solar photovoltaic (PV) energy generation, and PV-first smart charging scheduling and cost calculation.The realistic daily energy profiles were developed using real data on fifty EVs provided in the workplace. In the case of EV demand forecasting five deep learning models were tested, LSTM, GRU, TCN, Transformer Encoder and a Hybrid LightGBM-LSTM model where our proposed hybrid model had the lowest RMSE value 0.4049 and R2 value 0.8608. In the case of PV generation forecasting, eight ML models were experimented in the form of Random Forest, Decision Tree, Gradient Boosting, Linear Regression, LSTM, GRU, TCN as well as the hybrid model. Again, the highest PV prediction accuracy was obtained in Hybrid LightGBM + LSTM with RMSE value 273.55 and the R2 value 0.9321.The predicted EV and PV forecasts were combined in a PV- first scheduler that gave preference to solar power over grid power. In 7 days, the system made 41.87% cost savings and 52.3% contribution of solar; in 30 days, it made 17% savings under flat pricing and 24.9% savings under TOU that is aided by 37.7% renewable contribution. These findings indicate that dual hybrid forecasting and PV-first smart charging scheduling are effective in the reduction of charging expenses, minimization of grid reliance,minimize CO2 and in line with SDG 7 and SDG 13 on clean and sustainable energy.