![]() ![]() ![]() ![]() However, several challenges remain such as issues related to data quality or scalability which should be addressed through further research into tailored machine-learning methods. ![]() Additionally, it highlights the advantages of this approach including optimized energy scheduling, reduced costs, improved grid stability, and enhanced renewable integration. It discusses the importance of data preprocessing and feature selection techniques for improving model performance, as well as incorporating weather data, historical energy consumption patterns, and other relevant factors. This review emphasizes the potential of machine learning to enable intelligent energy forecasting and load prediction in smart grids. Subsequently, it examines how machine learning techniques can be applied in intelligent energy forecasting and load prediction tasks through support vector machines (SVM), neural networks (NNs), decision trees (DTs), or ensemble methods. The paper begins by providing an overview of smart grid components such as meters, sensors, communication networks, etc., before discussing the difficulties posed by traditional forecasting methods due to nonlinear energy patterns or uncertain renewable generation output. This review evaluates the potential of machine learning algorithms for intelligent energy forecasting and load prediction in smart grids, aiming to revolutionize the future of power systems. Machine learning offers a powerful toolkit to leverage data for accurate predictions and optimize real-time consumption decisions. Smart grids have become a vital part of modern electricity management as they enable integration with renewable energy sources, demand-response programs, and efficient load balancing. ![]()
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