Machine Learning Based Demand Supply System of an Airport to Minimize Operational Cost

Innovation in Electric Vehicle (EV) Battery and technological boost in charging infrastructure has proven the potential use of EV as Energy Storage Device (ESD) in Vehicle to Grid (V2G) mode to smooth the load curve. The Energy demand-supply system of a critical load i.e. an Airport industry has been analysed in this paper. This paper proposes a methodology to supply the demand combining three different energy sources - Solar, Hydro and EV in V2G mode as ESD and to decrease dependency on conventional supply i.e. state electricity board (SEB) and thereby focusing on reduction of overall operational cost as well as carbon emission. This work considers the revenue from carbon market as well. Day-ahead Load and solar energy forecasting using ANN with correlation analysis and State of Charge (SOC) prediction of EV using XGBoost has been used for proper planning.