This book explores the theoretical background and provides an experimental analysis of using natural energy resources in sustainable building design. It brings together an international group of contributors focusing on ways natural energy, lighting, and ventilation can improve the performance of electrical, lighting, and mechanical systems. Contributions explore how natural resources can contribute to sustainable development goals while meeting energy demands and maintaining acceptable interior air quality and natural illumination needs. Coverage includes green building design, renewable energy integration, photovoltaic systems, small-scale wind turbines, natural lighting, and natural ventilation.  Natural Energy, Lighting, and Ventilation in Sustainable Buildings offers practical and promising solutions for novel challenges in sustainable design for electrical engineers, energy engineers, architectural engineers, and related professionals, as well as researchers and developers from engineering science.

This book evaluates the role of innovative machine learning and deep learning methods in dealing with power system issues, concentrating on recent developments and advances that improve planning, operation, and control of power systems. Cutting-edge case studies from around the world consider prediction, classification, clustering, and fault/event detection in power systems, providing effective and promising solutions for many novel challenges faced by power system operators. Written by leading experts, the book will be an ideal resource for researchers and engineers working in the electrical power engineering and power system planning communities, as well as students in advanced graduate-level courses.

This book discusses the optimal design and operation of multi-carrier energy systems, providing a comprehensive review of existing systems as well as proposing new models. Chapters cover the theoretical background and application examples of interconnecting energy technologies such as combined heat and power plants, natural gas-fired power plants, power to gas technology, hydropower plants, and water desalination systems, taking into account the operational and technical constraints of each interconnecting element and the network constraint of each energy system. This book will be a valuable reference for power network and mechanical system professionals and engineers, electrical power engineering researchers and developers, and professionals from affiliated power system planning communities. Provides insight on the design and operation of multi-carrier energy systems; Covers both theoretical aspects and technical applications; Includes case studies to help apply concepts to real engineering situations.

This book discusses the optimal design and operation of multi-carrier energy systems, providing a comprehensive review of existing systems as well as proposing new models. Chapters cover the theoretical background and application examples of interconnecting energy technologies such as combined heat and power plants, natural gas-fired power plants, power to gas technology, hydropower plants, and water desalination systems, taking into account the operational and technical constraints of each interconnecting element and the network constraint of each energy system. This book will be a valuable reference for power network and mechanical system professionals and engineers, electrical power engineering researchers and developers, and professionals from affiliated power system planning communities. Provides insight on the design and operation of multi-carrier energy systems; Covers both theoretical aspects and technical applications; Includes case studies to help apply concepts to real engineering situations.

This book discusses the recent developments in robust optimization (RO) and information gap design theory (IGDT) methods and their application for the optimal planning and operation of electric energy systems. Chapters cover both theoretical background and applications to address common uncertainty factors such as load variation, power market price, and power generation of renewable energy sources. Case studies with real-world applications are included to help undergraduate and graduate students, researchers and engineers solve robust power and energy optimization problems and provide effective and promising solutions for the robust planning and operation of electric energy systems.

This book evaluates the role of innovative machine learning and deep learning methods in dealing with power system issues, concentrating on recent developments and advances that improve planning, operation, and control of power systems. Cutting-edge case studies from around the world consider prediction, classification, clustering, and fault/event detection in power systems, providing effective and promising solutions for many novel challenges faced by power system operators. Written by leading experts, the book will be an ideal resource for researchers and engineers working in the electrical power engineering and power system planning communities, as well as students in advanced graduate-level courses.