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Full Description
The book highlights how technologies including artificial intelligence and machine learning are transforming renewable energy technologies and enabling the development of new solutions. It further discusses how smart technologies are employed to optimize energy production and storage, enhance energy efficiency, and improve the overall sustainability of energy systems.
This book:
Discusses artificial intelligence-based techniques, namely, neural networks, fuzzy expert systems, optimization techniques, and operational research
Showcases the importance of artificial intelligence and machine learning in the energy market, demand analysis, and forecasting of renewable energy applications
Illustrates strategies for sustainable development using artificial intelligence and machine learning applications
Presents applications of artificial intelligence in the domain of electronics transformation and development, smart cities, and renewable energy utilization
Highlights the role of artificial intelligence in solving problems such as image and signal processing, smart weather monitoring, smart farming, and distributed energy sources
It is primarily written for senior undergraduates, graduate students, and academic researchers in diverse fields, including electrical, electronics and communications, energy, and environmental engineering.
Contents
1. Comprehensive Framework and Classification of Advanced Artificial Intelligence and Machine Learning Modelling Techniques. 2. Sustainable Development Using Renewable Energy Sources: Artificial Intelligence & Machine Learning contribution. 3. Artificial Intelligence as a tool for building more resilient cities in the climate change era: A systematic literature review. 4. Achieving Sustainable Development Goals through Knowledge Management in Industry 4.0: A Critical Review from the Perspective of Bangladesh. 5. Harmonizing Innovation and Accountability: The Intersection of Artificial Intelligence and Machine Learning in Sustainable Development. 6. A New Innovative Methodology for Photovoltaic Integration on Rooftops for Cost Reduction and Reduced Grid Dependency. 7. A Residual Deep Neural Network Based Non-Invasive system for Tomato Crop Health Monitoring, Disease Type Identification and Remedial Measures. 8. Advancements in Healthcare: Machine Learning Applications for quick Disease Diagnostics and Individualized Treatment. 9. IoT Based Home Automation using NODEMCU. 10. Stabilization and Synchronization of Chen-Lee Chaotic System using Sliding Mode Control Approach. 11. Application of Machine Learning Models for Power Systems Security Assessment. 12. Machine learning and deep learning models for effective forecasting of renewable energy generation. 13. Unlocking Predictive Potential: An Innovative Approach for Accuracy Enhancement in Software Effort Estimation. 14. Deep Learning-based Approach to Predict Software Faults
