A Brief Review on Accuracy Level of Smart and Micro Grid Systems Use of ML and DL Algorithms Through Hybrid Integration

Forecasting of energy is a crucial component in overcoming the challenges of smart grid mechanisms, which include functions such as demand-side management, load reduction, and desirable allocation. The most significant obstacle battling today's grid has to do with efficient forecasting management for restricted predictive inconsistencies. This paper provides a comprehensive and application-focused review of advanced prediction strategies for smart grid systems, as well as the latest advances in the use of Machine Learning (ML) and Deep Learning (DL) approaches. It also discusses the significance of hybridized techniques in improving the accuracy of forecasts. A study of comparison using an hourly energy consumption database has been carried out to evaluate the effectiveness of ML and DL techniques. The analysis' findings highlight hybrid methods' superior efficiency when compared to individual ML, DL, and standard statistical methods.