Intelligent Management of Energy Systems

Nyhet

Häftad, Engelska, 2026

Av Md Shafiullah, Md Shafiul Alam, Asif Islam, Minhajul Islam Ukil, MD Shafiullah, MD Shafiul Alam

2 439 kr

Kommande

Intelligent Management of Energy Systems provides a holistic overview of the opportunities for utilising artificial intelligence to optimize modern energy systems, including system dynamics, components, and business perspectives. This book begins with an overview of energy system dynamics, followed by detailed step-by-step breakdowns of critical artificial intelligence and machine learning techniques, as well as methods for assessing greenhouse-gas emissions. Further chapters spotlight microgrids, high-voltage testing for essential components such as circuit breakers and transformers, and perspectives from business and social disciplines. Designed for students, engineers, and researchers, Intelligent Management of Energy Systems combines clear, foundational methodologies, technical implementation, and business perspectives: a robust toolkit for AI utilization in energy system optimization.

Includes theory and practice from essential techniques to business considerations
Provides access to original code and MATLAB files, for practical learning and replication
Supports students and professionals in developing resilient, sustainable energy systems

Produktinformation

Utgivningsdatum2026-11-01
Mått152 x 229 x undefined mm
Vikt450 g
FormatHäftad
SpråkEngelska
Antal sidor300
FörlagElsevier Science
ISBN9780443405396

Tillhör följande kategorier

Energiteknik inom Naturvetenskap och teknik

Dr. Md Shafiullah is currently working as a faculty member in the Interdisciplinary Research Center for Renewable Energy and Power Systems (IRC-REPS) at King Fahd University of Petroleum & Minerals (KFUPM). He received a Ph.D. in Electrical Engineering (Electrical Power & Energy Systems) from KFUPM in 2018. Prior to that, he received the B.Sc. and M.Sc. degrees in Electrical & Electronic Engineering (EEE) from Bangladesh University of Engineering & Technology (BUET) in 2009 and 2013, respectively. He demonstrated his research contributions in 70+ scientific articles (peer-reviewed journals, international conference proceedings, and book chapters). His research interest includes power system fault diagnosis, grid integration of renewable energy resources, power system stability and quality analysis, and machine learning techniques. He received the best research paper awards in two different IEEE flagship conferences (ICEEICT 2014 in Bangladesh and CAIDA 2021 in Saudi Arabia). Md. Shafiul Alam is a Research Engineer with the Applied Research Centre for Environment and Marine Studies of KFUPM, Saudi Arabia. His research interests include energy and environment, management of greenhouse gas emissions, data analysis, integration of renewable energy sources into the utility grid, AC/DC microgrids, high-voltage DC transmission, and machine learning. Asif Islam is a Research Engineer and Assistant Professor at the Interdisciplinary Research Center for Sustainable Energy Systems of King Fahd University of Petroleum and Minerals, Saudi Arabia. Previously he worked as a T&E Ops. Quality Manager at Boeing-Australia (2021-2023), as a Test Engineer of Power System Protection and HV Test Laboratory at NOJA Power (2018-2021), as an R&D Engineer at NOJA Power (2013-2017), as an Assistant Engineer at Power Grid Company of Bangladesh (PGCB) Ltd. (2012-2013) and as a Testing Engineer at Energypac Engineering Ltd. (2009- 2012). His research interests include power system protection, high voltage engineering, transformer and switchgear, and quality management systems. Minhajul Islam Ukil is an Assistant Professor at the Department of Management and Marketing, King Fahd University of Petroleum and Minerals, Saudi Arabia. Previously, Dr. Ukil held academic positions at BRAC University (Bangladesh) and the University of Adelaide (Australia). His research interests include social entrepreneurship and entrepreneurial dynamics, focusing on energy business and economics.

1. Energy System Dynamics2. Artificial Intelligence and Machine Learning Techniques3. Energy and GHG Trends Analysis with Ensemble Models4. Intelligent Energy Management in Microgrids5. AI-Assisted Community Microgrid Energy Management6. Artificial Intelligence in High Voltage Testing: Circuit Breakers7. Artificial Intelligence in High Voltage Testing: Transformers8. A Business Model for AI-based Smart Energy Systems9. Public Perceptions and Ethical Considerations concerning AI-based Smart Energy Systems