AI-Based Renewable Energy Systems by Kashif Naseer Qureshi (.PDF)
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Artificial Intelligence-Based Renewable Energy Systems: Standards, Communication Systems, and Data Networks by
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Overview: Artificial Intelligence-based Renewable Energy Systems: Standards, Communication Systems, and Data Networks provides a comprehensive guide to integrating AI into renewable energy systems, addressing both fundamental principles and practical applications. Sections explore the evolution of AI in energy-efficient systems, advanced applications, architectural frameworks, standards, and distributed renewable energy networks while also delving into data communication systems, including networking protocols, advanced learning techniques, blockchain applications, big data analytics, green communication solutions, cybersecurity, and privacy challenges. Combining theoretical foundations with practical insights, this book offers students, researchers, and engineers a holistic perspective on AI communication and data networks for renewable energy systems. Common ML algorithms used in these applications are SVM, decision tree, RF, K-means clustering, and gradient boosting. DL is a subset of ML by using the multilayered neural networks to model the complex data relationship. The DL methods excel at complex data processing, especially nonlinear and unstructured data such as time-series signals, images, and sensor outputs. The popular DL architectures are CNNs, RNNs, LSTMs, and autoencoders. The emerging and advanced technologies like blockchain, quantum computing, and 5G/6G edge computing are set to revolutionize the energy systems for better analysis. The deployment of 5G/6G will improve the existing energy systems with better communication processes and offer ultralow latency, high data throughput, and better controlling and monitoring mechanisms. These technologies are better for real-time and time-sensitive operations.
Genre: Non-Fiction > Tech & Devices
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