Machine learning techniques can improve the application and administration of renewable energy sources, making them more viable options for meeting the world's energy needs. In solar energy systems, machine learning algorithms enhance solar panel performance, increase energy forecasting, and optimize energy storage systems.
Hence the system can be considered to have the potential as the next generation of air-conditioning systems that has the advantage of reducing energy consumption to meet the cooling load while employing the abundant solar energy resources.
Therefore, incorporating the energy storage system (ESS) into the energy systems could be a great strategy to manage these issues and provide the energy systems with technical, economic, and environmental benefits.
AI has proven indispensable in optimizing energy storage systems (ESS), which are essential for power system stability and the use of renewable energy. Abdalla et al. (2021) investigated the purpose, design, and optimization of ESS in power systems.
AI algorithms have improved the performance and cost-effectiveness of photovoltaic systems, deep learning models have accurately predicted solar energy generation, and the Internet of Things has accelerated the detection and repair of defective solar cells.
Education and awareness activities are also required to encourage sustainable lifestyles and the usage of renewable energy. In conclusion, integrating solar and wind energy into existing power systems is essential for a sustainable energy future.