Prediction of electricity consumption and hydropower production in the smart power grid based on the gated recurrent unit neural network and modified future search algorithm

Precise prediction of electricity usage and hydroelectric power production can enhance energy distribution efficiency, minimize waste, and enhance general grid performance. Moreover, utilizing the Gated Recurrent Unit (GRU) Neural Network and Modified Future Search Algorithm (MFSA) can offer more accurate analysis and forecasts, and facilitates improved decision-making and allocation of resources. This investigation aims to develop an improved model based on GRU and MFSA for examining and forecasting energy generation and electricity usage in a smart power grid. This study employs economic and social data to predict patterns of long-term electricity usage, while climatic data is employed as input for the optimum model for simulation of electricity generation, which concentrates particularly on hydropower production. The developed optimized model has been compared to original deep learning and optimized methods to demonstrate its enhanced efficacy and superiority. According to the investigation, the obtained results show how effective the optimal method is in accurately forecasting electricity usage and hydropower generation.