Day?ahead scheduling of a hybrid renewable energy system based on generation forecasting using a deep?learning approach

A significant amount of electricity in numerous regions worldwide is used for lighting roads, squares, and other public spaces. Renewable energy can contribute notably to usage lighting. This paper focuses on the day-ahead scheduling a hybrid renewable system (HRES) exploiting solar–wind potential meet electrical needs The studied HRES provides Wi-Fi hotspot charging end users has an storage that ensures reliable supply without interruptions. based generation forecasting by using deep-learning approach. Particularly, long short-term memory model utilized, considering fact it able perceive long-term dependencies among time series. Moreover, model's performance investigated through determination diverse inputs: (a) historical data, (b) weather predictions, (c) data with predictions. Multiple scenarios consumption are assumed applied optimize scheduling. new recommendation method proposed scheduling, utilizing power forecasts achieve optimum operation savings. results point out utilization controls loads when shortage battery capacity forecasted day ahead, leading savings minimizing demand from grid.