Achieving Grid Resilience and Stability with Horizontal Federated Learning Based Energy Demand Forecasting

The increasing popularity of electric vehicles is set to transform the dynamics of energy consumption. Demand forecasting of electric vehicle charging stations guarantees effective distribution of the available power supply. It helps to control the power demand of several charging stations to maximize their use. The power demand may outweigh the supply when several electric vehicles charge concurrently at a charging station or a collection of charging stations. Thus, the work suggests a horizontal federated learning method for energy demand forecasting to guarantee the effective and smart distribution of electric power to all active charging stations. Using horizontal federated learning, in which charging station data remains at the stations themselves, the model guarantees accurate demand forecasts and enhances grid resilience and stability. Using this distributed method, the smart grid can dynamically adjust to demand fluctuations, ensuring the best use of the available electricity resources and reducing the risk of system overload.