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