Hybrid AI Approaches for Energy Management and Smart Grid Optimization
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The increasing integration of renewable energy sources and the growing complexity of power grids demand intelligent and adaptive energy management systems. This chapter explores the application of hybrid Artificial Intelligence (AI) approaches for optimizing energy management and enhancing the stability of smart grids. We present a comprehensive framework that combines deep learning models, such as Long ShortTerm Memory (LSTM) networks and Convolutional Neural Networks (CNNs), with machine learning techniques like Support Vector Machines (SVM) and ensemble methods, and optimization algorithms including Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). The proposed hybrid model is designed to address critical challenges in smart grid operations, including accurate load forecasting, efficient demand-side management, and real-time grid optimization. A synthetic dataset, simulating a year of hourly smart grid data, is used to train and evaluate the models. The results demonstrate that the hybrid AI approach significantly outperforms individual models in terms of prediction accuracy and optimization efficiency, achieving a Mean Squared Error (MSE) of 0.000144 and an R2 score of 0.9973. The chapter provides a detailed analysis of the methodology, simulation results, and a discussion of the practical implications for developing nextgeneration intelligent energy management systems.
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Principles of Hybrid Intelligent Systems
