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Full Description
Engineering Applications of AI for Demand Forecasting explores how Artificial Intelligence enhances prediction accuracy across modern engineering systems. As industries move toward Industry 4.0, the book highlights the role of AI in processing large, dynamic datasets for smarter decision-making. This book brings together contemporary research that demonstrates AI's ability to enhance precision, efficiency, and adaptability in diverse forecasting environments. By covering cybersecurity analytics, anomaly detection, logistics forecasting, sustainable supply chain management, and predictive maintenance, it demonstrates AI's versatility in complex environments. The book also showcases AI applications in renewable energy forecasting, peak load prediction, smart meter analytics, and prosumer-driven demand modeling. Combining theory with practical case studies, it serves as a valuable resource for engineers, researchers, practitioners, and students.
Contents
Foreword. Preface. List of Abbreviations. AI-Driven Demand Forecasting in Industry 4.0: Techniques, Applications, and Future Trends. AI Engineering for Meeting the Demand of Open-Source Cyber Analytics. Application of AI for Anomaly Detection and Failure Prediction to Optimize Cost of Service. Generative AI Based Demand Forecasting for Third-Party Logistics. Demand Forecasting for Sustainable Supply Chain Management Leveraging AI. Demand Forecasting for AI in Supply Chain Management. Artificial Intelligence Based Alternate Energy Demand Forecasting Incorporating Weather Data Analysis. Electricity Demand Forecasting during Special Events Using AI. Predicting Peak Energy Demand Using Machine Learning Techniques for Efficient Electricity Supply Management. AI-based Electrical Load Forecasting for Residential Sector Using Smart Meter Data. Prosumer Electricity Demand Forecasting Using Artificial Intelligence-based Algorithms Incorporating Meteorological Data. Index.
