The upcoming-generation energy grid is often known to be the “smart grid” or “intelligent grid”. It is anticipated to solve the existing infrastructure's fundamental flaws. Smart grid technology shows us a solution for improved electric energy generation as well as an efficient means for transmitting and distributing this electricity.
Energy generation and management are relevant for both utilities and electricity users, and they can be improved by incorporating sophisticated technology on smart grid.
However, this research aims to enhance the efficiency of solar power generation systems in a smart grid context using machine learning hybrid models such as Hybrid Convolutional-Recurrence Net (HCRN), Hybrid Convolutional-LSTM Net (HCLN), and Hybrid Convolutional-GRU Net (HCGRN).
A smart grid is required for improved energy control, the integration of renewable energy sources, and the response to surges in energy demand . Renewable energy sources (RES) are more sustainable, reliable, and cost effective than non-renewable energy sources (NRES).
Smart grids are known to be next-generation conventional grids due to the information flow capabilities and two-ways power supply. It integrates the actions of all users and facilitates the bidirectional operation of the distribution of the system for delivering a sustainable and economic electric supply.
The future smart grid is facilitated by the efficient demand response mechanism (DRM) which is based on the energy consumers capable of providing a flexible schedule for energy consumption and supply . Since smart grids are under the threat of cyber terrorism, cyber security measure is being developed. Malicious attacks need to be prevented.