Appasani B , Jha a Engineering Applications of AI for Demand Forecasting 2026 [TorrentX]

Textbook in PDF format
Engineering Applications of AI for Demand Forecasting explores how Artificial Intelligence (AI) 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. 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.
The transformative potential of Artificial intelligence (AI) and Machine Learning (ML) for demand forecasting in the manufacturing sector is becoming undeniably evident, particularly in the evolving framework of Industry 4.0. As manufacturers strive to optimize their supply chains, the integration of advanced technologies such as AI-powered predictive analytics has emerged as a critical differentiator in enhancing forecasting precision and operational efficiency. Traditional demand forecasting methods—typically relying on simplistic analyzes of historical data are increasingly proving inadequate in the face of modern, complex supply chain dynamics.
In contrast, AI-driven models, including Deep Learning (DL) architectures such as autoencoders, convolutional neural networks (CNNs), and recurrent neural networks (RNNs) have demonstrated significant improvements by automatically extracting complex features and patterns from vast datasets, which conventional approaches tend to overlook. This shift toward AI-driven solutions reflects a foundational change in how businesses approach demand forecasting, allowing them to respond more effectively to market fluctuations and consumer behavior.
Preface
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 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
AI-based Electrical Load Forecasting for Residential Sector Using Smart Meter Data
Prosumer Electricity Demand Forecasting Using Artificial Intelligence-based Algorithms Incorporating Meteorological Data