Real-World Applications of Artificial Intelligence and Machine Learning in Power Systems: A Code Approach

Dive into the cutting-edge world of artificial intelligence and machine learning with this comprehensive guide, designed for both novices and experts alike. This book meticulously explores the core principles, development, and practical applications of AI and ML technologies.Beginning with an introduction to AI, the book navigates through the evolution and principles of artificial intelligence, delving deep into various neural network architectures, including feedforward, convolutional, recurrent, and generative adversarial networks. Each chapter is enriched with practical examples such as rainfall prediction, image analysis, sales forecasting, and financial data analysis, making complex concepts accessible and relatable. This book also offers an extensive overview of essential machine learning libraries and tools, including NumPy, Pandas, TensorFlow, and PyTorch. Readers will gain insights into both supervised and unsupervised learning algorithms, from logistic regression and decision trees to k-means clustering and Gaussian mixture models.In the latter part, the focus shifts to real-world applications of machine learning in power systems, renewable energy, electric vehicles, fuel cells, and hydrogen production. Topics such as fault detection in power grids, energy theft detection, solar and wind energy forecasting, and predictive maintenance for electric vehicles and fuel cells are comprehensively covered, demonstrating the transformative impact of ML in these sectors. Whether you are an aspiring data scientist, an academic, or a professional in the field, this book is your essential resource for mastering the intricacies of AI and ML and applying these technologies to solve real-world problems.