Engineering Applications of AI for Demand Forecasting
Published on 9. June 2026
296 pages
978-1-040-52535-7 (ISBN)
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.
More details
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Illustrations
38 Tables, black and white; 80 Line drawings, black and white; 29 Halftones, black and white; 109 Illustrations, black and white
File size
23,51 MB
ISBN-13
978-1-040-52535-7 (9781040525357)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Bhargav Appasani | Amitkumar Vidyakant Jha
Engineering Applications of AI for Demand Forecasting
Book
approx. 06/2026
1st Edition
CRC Press
€154.50
Not yet published
Persons
Dr. Bhargav Appasani received the Ph.D. (Engg.) degree from Birla Institute of Technology, Mesra, India. He is currently an Associate Professor with the School of Electronics Engineering, KIIT University, Bhubaneswar, India. He has published more than 160 articles in international journals and conference proceedings. He has also published six book chapters with Springer and Elsevier. He has authored one book and has edited three books. He also has a patent filed to his credit. He is an academic editor of the Journal of Electrical and Computer Engineering (Wiley) and reviewer for IEEE Transactions on Smart Grid, IEEE Transactions on Antennas and Propagation, and IEEE Access. Dr. Appasani has taught many courses such as machine learning, data structure algorithm, microwave, control system, etc. for several years.
Dr. Amitkumar Vidyakant Jha received M.Tech degree from Indian Institute of Industrial Technology Guwahati, India and Ph.D. degree from Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, India. He works as an associate professor at the KIIT. He has authored more than 60 articles in international journals, and conference proceedings. He has also authored 4 book chapters published by Springer and Elsevier. He has also edited two books which have been published by Nova Science and Taylor & Francis. To his credit, he has 4 patents. He has been honored with various awards including Best paper award in IEEE ECAI conference, Pitesti, Romania, Best paper award in IEEE CEES, Tokyo, Japan, Best Paper Award in IEEE STPEC 2023, KIIT University, India. He serves also as an editor to Scientific Reports journal of Springer Nature portfolio. He is an active reviewer of many international journals from the portfolio of IEEE, Springer, Elsevier, Wiley, Frontier, IOP, etc. Dr. Jha has vast teaching experience for many core engineering subjects such as data communication and networking, computer networks, data structure & algorithms, wireless sensor networks, digital system design, etc. He is a member of various professional societies including a life-time member of Indian Science Congress (ISC), International Association of Engineers (IAENG), and World Leadership Academy.
Dr. Amitkumar Vidyakant Jha received M.Tech degree from Indian Institute of Industrial Technology Guwahati, India and Ph.D. degree from Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, India. He works as an associate professor at the KIIT. He has authored more than 60 articles in international journals, and conference proceedings. He has also authored 4 book chapters published by Springer and Elsevier. He has also edited two books which have been published by Nova Science and Taylor & Francis. To his credit, he has 4 patents. He has been honored with various awards including Best paper award in IEEE ECAI conference, Pitesti, Romania, Best paper award in IEEE CEES, Tokyo, Japan, Best Paper Award in IEEE STPEC 2023, KIIT University, India. He serves also as an editor to Scientific Reports journal of Springer Nature portfolio. He is an active reviewer of many international journals from the portfolio of IEEE, Springer, Elsevier, Wiley, Frontier, IOP, etc. Dr. Jha has vast teaching experience for many core engineering subjects such as data communication and networking, computer networks, data structure & algorithms, wireless sensor networks, digital system design, etc. He is a member of various professional societies including a life-time member of Indian Science Congress (ISC), International Association of Engineers (IAENG), and World Leadership Academy.
Content
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.
