Performance optimization of hybrid renewable energy systems with real-time load forecasting using grey wolf-based predictive models

The performance optimization of hybrid renewable energy systems (HRES) 
is crucial for enhancing the efficiency, reliability, and sustainability of 
energy production. This study focuses on the integration of real-time load 
forecasting prediction using a grey wolf optimization (GWO)-based 
predictive model. The proposed methodology aims to address the challenges 
associated with the intermittent nature of renewable energy sources, such as 
solar and wind power, by providing accurate forecasts for load demands and 
solar irradiance. Real-time data from sensors and environmental parameters 
are incorporated to forecast the energy load and solar irradiance over short
term periods, which are then used to optimize the energy storage and 
generation components of the HRES. The GWO algorithm, known for its 
high accuracy and computational efficiency, is employed to optimize the 
dispatch of power from various sources while minimizing energy losses and 
ensuring system stability. The integration of GWO with real-time forecasting 
not only enhances the predictive capability of the system but also improves 
the overall economic viability of HRES by reducing operational costs and 
carbon emissions. This study demonstrates the potential of using intelligent 
optimization techniques and real-time forecasting for the sustainable 
operation of hybrid renewable energy systems, contributing to the 
development of smarter and more resilient energy grids.