Advances In Digitalization And Machine Learning For Integrated Building Transportation Energy Systems

Author : Yuekuan Zhou,Jinglei Yang,Guoqiang Zhang,Peter D. Lund
Publisher : Elsevier
Page : 302 pages
File Size : 53,5 Mb
Release : 2023-11-20
Category : Computers
ISBN : 9780443131783

Get Book

Advances in Digitalization and Machine Learning for Integrated Building-Transportation Energy Systems by Yuekuan Zhou,Jinglei Yang,Guoqiang Zhang,Peter D. Lund Pdf

Advances in Digitalization and Machine Learning for Integrated Building-Transportation Energy Systems examines the combined impact of buildings and transportation systems on energy demand and use. With a strong focus on AI and machine learning approaches, the book comprehensively discusses each part of the energy life cycle, considering source, grid, demand, storage, and usage. Opening with an introduction to smart buildings and intelligent transportation systems, the book presents the fundamentals of AI and its application in renewable energy sources, alongside the latest technological advances. Other topics presented include building occupants’ behavior and vehicle driving schedule with demand prediction and analysis, hybrid energy storages in buildings with AI, smart grid with energy digitalization, and prosumer-based P2P energy trading. The book concludes with discussions on blockchain technologies, IoT in smart grid operation, and the application of big data and cloud computing in integrated smart building-transportation energy systems. A smart and flexible energy system is essential for reaching Net Zero whilst keeping energy bills affordable. This title provides critical information to students, researchers and engineers wanting to understand, design, and implement flexible energy systems to meet the rising demand in electricity. Introduces spatiotemporal energy sharing with new energy vehicles and human-machine interactions Discusses the potential for electrification and hydrogenation in integrated building-transportation systems for sustainable development Highlights key topics related to traditional energy consumers, including peer-to-peer energy trading and cost-benefit business models

Author : Acharjya, Pinaki Pratim,Koley, Santanu,Barman, Subhabrata
Publisher : IGI Global
Page : 351 pages
File Size : 41,6 Mb
Release : 2024-05-01
Category : Technology & Engineering
ISBN : 9798369323564

Get Book

Machine Learning and Computer Vision for Renewable Energy by Acharjya, Pinaki Pratim,Koley, Santanu,Barman, Subhabrata Pdf

As the world grapples with the urgent need for sustainable energy solutions, the limitations of traditional approaches to renewable energy forecasting become increasingly evident. The demand for more accurate predictions in net load forecasting, line loss predictions, and the seamless integration of hybrid solar and battery storage systems is more critical than ever. In response to this challenge, advanced Artificial Intelligence (AI) techniques are emerging as a solution, promising to revolutionize the renewable energy landscape. Machine Learning and Computer Vision for Renewable Energy presents a deep exploration of AI modeling, analysis, performance prediction, and control approaches dedicated to overcoming the pressing issues in renewable energy systems. Transitioning from the complexities of energy prediction to the promise of advanced technology, the book sets its sights on the game-changing potential of computer vision (CV) in the realm of renewable energy. Amidst the struggle to enhance sustainability across industries, CV technology emerges as a powerful ally, collecting invaluable data from digital photos and videos. This data proves instrumental in achieving better energy management, predicting factors affecting renewable energy, and optimizing overall sustainability. Readers, including researchers, academicians, and students, will find themselves immersed in a comprehensive understanding of the AI approaches and CV methodologies that hold the key to resolving the challenges faced by renewable energy systems.

Author : Denis Sidorov
Publisher : MDPI
Page : 272 pages
File Size : 46,9 Mb
Release : 2020-12-08
Category : Technology & Engineering
ISBN : 9783039433827

Get Book

Machine Learning for Energy Systems by Denis Sidorov Pdf

This volume deals with recent advances in and applications of computational intelligence and advanced machine learning methods in power systems, heating and cooling systems, and gas transportation systems. The optimal coordinated dispatch of the multi-energy microgrids with renewable generation and storage control using advanced numerical methods is discussed. Forecasting models are designed for electrical insulator faults, the health of the battery, electrical insulator faults, wind speed and power, PV output power and transformer oil test parameters. The loads balance algorithm for an offshore wind farm is proposed. The information security problems in the energy internet are analyzed and attacked using information transmission contemporary models, based on blockchain technology. This book will be of interest, not only to electrical engineers, but also to applied mathematicians who are looking for novel challenging problems to focus on.

Author : O.V. Gnana Swathika,K. Karthikeyan,Sanjeevikumar Padmanaban
Publisher : CRC Press
Page : 421 pages
File Size : 47,7 Mb
Release : 2022-02-24
Category : Technology & Engineering
ISBN : 9781000537949

Get Book

Smart Buildings Digitalization by O.V. Gnana Swathika,K. Karthikeyan,Sanjeevikumar Padmanaban Pdf

This book discusses various artificial intelligence and machine learning applications concerning smart buildings. It includes how renewable energy sources are integrated into smart buildings using suitable power electronic devices. The deployment of advanced technologies with monitoring, protection, and energy management features is included, along with a case study on automation. Overall, the focus is on architecture and related applications, such as power distribution, microgrids, photovoltaic systems, and renewable energy aspects. The chapters define smart building concepts and their related benefits. FEATURES Discusses various aspects of the role of the Internet of things (IoT) and machine learning in smart buildings Explains pertinent system architecture and focuses on power generation and distribution Covers power-enabling technologies for smart cities Includes photovoltaic system-integrated smart buildings This book is aimed at graduate students, researchers, and professionals in building systems engineering, architectural engineering, and electrical engineering.

Author : Denis N. Sidorov
Publisher : Unknown
Page : 272 pages
File Size : 43,6 Mb
Release : 2020
Category : Electronic
ISBN : 3039433830

Get Book

Machine Learning for Energy Systems by Denis N. Sidorov Pdf

This volume deals with recent advances in and applications of computational intelligence and advanced machine learning methods in power systems, heating and cooling systems, and gas transportation systems. The optimal coordinated dispatch of the multi-energy microgrids with renewable generation and storage control using advanced numerical methods is discussed. Forecasting models are designed for electrical insulator faults, the health of the battery, electrical insulator faults, wind speed and power, PV output power and transformer oil test parameters. The loads balance algorithm for an offshore wind farm is proposed. The information security problems in the energy internet are analyzed and attacked using information transmission contemporary models, based on blockchain technology. This book will be of interest, not only to electrical engineers, but also to applied mathematicians who are looking for novel challenging problems to focus on.

Author : Krishna Kumar,Ram Shringar Rao,Omprakash Kaiwartya,Shamim Kaiser,Sanjeevikumar Padmanaban
Publisher : Academic Press
Page : 418 pages
File Size : 40,8 Mb
Release : 2022-03-18
Category : Science
ISBN : 9780323914284

Get Book

Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies by Krishna Kumar,Ram Shringar Rao,Omprakash Kaiwartya,Shamim Kaiser,Sanjeevikumar Padmanaban Pdf

Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies analyzes the changes in this energy generation shift, including issues of grid stability with variability in renewable energy vs. traditional baseload energy generation. Providing solutions to current critical environmental, economic and social issues, this book comprises various complex nonlinear interactions among different parameters to drive the integration of renewable energy into the grid. It considers how artificial intelligence and machine learning techniques are being developed to produce more reliable energy generation to optimize system performance and provide sustainable development. As the use of artificial intelligence to revolutionize the energy market and harness the potential of renewable energy is essential, this reference provides practical guidance on the application of renewable energy with AI, along with machine learning techniques and capabilities in design, modeling and for forecasting performance predictions for the optimization of renewable energy systems. It is targeted at researchers, academicians and industry professionals working in the field of renewable energy, AI, machine learning, grid Stability and energy generation. Covers the best-performing methods and approaches for designing renewable energy systems with AI integration in a real-time environment Gives advanced techniques for monitoring current technologies and how to efficiently utilize the energy grid spectrum Addresses the advanced field of renewable generation, from research, impact and idea development of new applications

Author : Wenjuan Wang,Qasim Zaheer,Shi Qiu,Weidong Wang,Chengbo Ai,Jin Wang,Sicheng Wang,Wenbo Hu
Publisher : Springer Nature
Page : 232 pages
File Size : 52,6 Mb
Release : 2024-01-06
Category : Computers
ISBN : 9789819958047

Get Book

Digital Twin Technologies in Transportation Infrastructure Management by Wenjuan Wang,Qasim Zaheer,Shi Qiu,Weidong Wang,Chengbo Ai,Jin Wang,Sicheng Wang,Wenbo Hu Pdf

This book reveals the power of digital twin technologies in terms of optimizing the performance and maintenance of infrastructure assets. From roads, bridges, and tunnels to airports and smart cities, it will guide you through the latest advances in and use cases on this cutting-edge technology. You will come to understand the challenges involved in the development of digital twins and learn about the initiatives and projects underway to overcome them. Explore the potential of this technology in terms of reducing costs, improving system performance, and enhancing the overall infrastructure experience for users. Get ready to embark on a journey of understanding the future of transportation infrastructure management with digital twin technologies.

Author : Ajay Kumar Vyas,S. Balamurugan,Kamal Kant Hiran,Harsh S. Dhiman
Publisher : John Wiley & Sons
Page : 276 pages
File Size : 53,6 Mb
Release : 2022-03-02
Category : Computers
ISBN : 9781119761693

Get Book

Artificial Intelligence for Renewable Energy Systems by Ajay Kumar Vyas,S. Balamurugan,Kamal Kant Hiran,Harsh S. Dhiman Pdf

ARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY SYSTEMS Renewable energy systems, including solar, wind, biodiesel, hybrid energy, and other relevant types, have numerous advantages compared to their conventional counterparts. This book presents the application of machine learning and deep learning techniques for renewable energy system modeling, forecasting, and optimization for efficient system design. Due to the importance of renewable energy in today’s world, this book was designed to enhance the reader’s knowledge based on current developments in the field. For instance, the extraction and selection of machine learning algorithms for renewable energy systems, forecasting of wind and solar radiation are featured in the book. Also highlighted are intelligent data, renewable energy informatics systems based on supervisory control and data acquisition (SCADA); and intelligent condition monitoring of solar and wind energy systems. Moreover, an AI-based system for real-time decision-making for renewable energy systems is presented; and also demonstrated is the prediction of energy consumption in green buildings using machine learning. The chapter authors also provide both experimental and real datasets with great potential in the renewable energy sector, which apply machine learning (ML) and deep learning (DL) algorithms that will be helpful for economic and environmental forecasting of the renewable energy business. Audience The primary target audience includes research scholars, industry engineers, and graduate students working in renewable energy, electrical engineering, machine learning, information & communication technology.

Author : Alessandro Burgio,Zbigniew M. Leonowicz,Michal Jasinski
Publisher : Frontiers Media SA
Page : 211 pages
File Size : 47,9 Mb
Release : 2024-01-19
Category : Technology & Engineering
ISBN : 9782832543245

Get Book

Enabling technologies and business models for energy communities by Alessandro Burgio,Zbigniew M. Leonowicz,Michal Jasinski Pdf

Author : Marco Casini
Publisher : Woodhead Publishing
Page : 696 pages
File Size : 42,5 Mb
Release : 2021-11-24
Category : Technology & Engineering
ISBN : 9780128218037

Get Book

Construction 4.0 by Marco Casini Pdf

Developments in data acquisition technologies, digital information and analysis, automated construction processes, and advanced materials and products have finally started to move the construction industry - traditionally reluctant to innovation and slow in adopting new technologies - toward a new era. Massive changes are occurring because of the possibilities created by Building information modeling, Extended reality, Internet of Things, Artificial intelligence and Machine Learning, Big data, Nanotechnology, 3D printing, and other advanced technologies, which are strongly interconnected and are driving the capabilities for much more efficient construction at scale. Construction 4.0: Advanced Technology, Tools and Materials for the Digital Transformation of the Construction Industry provides readers with a state-of-the-art review of the ongoing digital transformation of the sector within the new 4.0 framework, presenting a thorough investigation of the emerging trends, technologies, and strategies in the fields of smart building design, construction, and operation and providing a comprehensive guideline on how to exploit the new possibilities offered by the digital revolution. It will be an essential reference resource for academic researchers, material scientists and civil engineers, undergraduate and graduate students, and other professionals working in the field of smart ecoefficient construction and cutting-edge technologies applied to construction. Provides an overview of the Construction 4.0 framework to address the global challenges of the buildingsector in the 21st century and an in-depth analysis of the most advanced digital technologies and systems forthe operation and maintenance of infrastructure, real estate, and other built assets Covers major innovations across the value chain, including building design, fabrication, construction, operationand maintenance, and end-of-life Illustrates the most advanced digital tools and methods to support the building design activity, includinggenerative design, virtual reality, and digital fabrication Presents a thorough review of the most advanced construction materials, building methods, and techniquesfor a new connected and automated construction model Explores the digital transformation for smart energy buildings and their integration with emerging smartgrids and smart cities Reflects upon major findings and identifies emerging market opportunities for the whole AECO sector

Author : S. Vijayalakshmi,Savita .,Balamurugan Balusamy,Rajesh Kumar Dhanaraj
Publisher : Springer Nature
Page : 318 pages
File Size : 40,8 Mb
Release : 2023-04-05
Category : Technology & Engineering
ISBN : 9783031150449

Get Book

AI-Powered IoT in the Energy Industry by S. Vijayalakshmi,Savita .,Balamurugan Balusamy,Rajesh Kumar Dhanaraj Pdf

AI-Powered IoT in the Energy Industry: Digital Technology and Sustainable Energy Systems looks at opportunities to employ cutting-edge applications of artificial intelligence (AI), the Internet of Things (IoT), and Machine Learning (ML) in designing and modeling energy and renewable energy systems. The book's main objectives are to demonstrate how big data can help with energy efficiency and demand reduction, increase the usage of renewable energy sources, and assist in transitioning from a centralized system to a distributed, efficient, and embedded energy system. Contributions cover the fundamentals of the renewable energy sector, including solar, wind, biomass, and hydrogen, as well as building services and power generation systems. Chapters also examine renewable energy property prediction methods and discuss AI and IoT prediction models for biomass thermal properties. ​Covers renewable energy sector fundamentals; Explains the application of big data in distributed energy domains; Discusses AI and IoT prediction methods and models.

Author : Rabindra Nath Shaw,Nishad Mendis,Saad Mekhilef,Ankush Ghosh
Publisher : Springer Nature
Page : 109 pages
File Size : 43,6 Mb
Release : 2021-05-12
Category : Technology & Engineering
ISBN : 9789811610110

Get Book

AI and IOT in Renewable Energy by Rabindra Nath Shaw,Nishad Mendis,Saad Mekhilef,Ankush Ghosh Pdf

This book presents the latest research on applications of artificial intelligence and the Internet of Things in renewable energy systems. Advanced renewable energy systems must necessarily involve the latest technology like artificial intelligence and Internet of Things to develop low cost, smart and efficient solutions. Intelligence allows the system to optimize the power, thereby making it a power efficient system; whereas, Internet of Things makes the system independent of wire and flexibility in operation. As a result, intelligent and IOT paradigms are finding increasing applications in the study of renewable energy systems. This book presents advanced applications of artificial intelligence and the internet of things in renewable energy systems development. It covers such topics as solar energy systems, electric vehicles etc. In all these areas applications of artificial intelligence methods such as artificial neural networks, genetic algorithms, fuzzy logic and a combination of the above, called hybrid systems, are included. The book is intended for a wide audience ranging from the undergraduate level up to the research academic and industrial communities engaged in the study and performance prediction of renewable energy systems.

Author : Mukhdeep Singh Manshahia,Valeriy Kharchenko,Gerhard-Wilhelm Weber,Pandian Vasant
Publisher : Springer Nature
Page : 302 pages
File Size : 44,6 Mb
Release : 2023-06-14
Category : Technology & Engineering
ISBN : 9783031264962

Get Book

Advances in Artificial Intelligence for Renewable Energy Systems and Energy Autonomy by Mukhdeep Singh Manshahia,Valeriy Kharchenko,Gerhard-Wilhelm Weber,Pandian Vasant Pdf

This book provides readers with emerging research that explores the theoretical and practical aspects of implementing new and innovative artificial intelligence (AI) techniques for renewable energy systems. The contributions offer broad coverage on economic and promotion policies of renewable energy and energy-efficiency technologies, the emerging fields of neuro-computational models and simulations under uncertainty (such as fuzzy-based computational models and fuzzy trace theory), evolutionary computation, metaheuristics, machine learning applications, advanced optimization, and stochastic models. This book is a pivotal reference for IT specialists, industry professionals, managers, executives, researchers, scientists, and engineers seeking current research in emerging perspectives in artificial intelligence, renewable energy systems, and energy autonomy.

Author : Ibrahim Dincer
Publisher : Elsevier
Page : 5543 pages
File Size : 47,8 Mb
Release : 2018-02-07
Category : Science
ISBN : 9780128149256

Get Book

Comprehensive Energy Systems by Ibrahim Dincer Pdf

Comprehensive Energy Systems, Seven Volume Set provides a unified source of information covering the entire spectrum of energy, one of the most significant issues humanity has to face. This comprehensive book describes traditional and novel energy systems, from single generation to multi-generation, also covering theory and applications. In addition, it also presents high-level coverage on energy policies, strategies, environmental impacts and sustainable development. No other published work covers such breadth of topics in similar depth. High-level sections include Energy Fundamentals, Energy Materials, Energy Production, Energy Conversion, and Energy Management. Offers the most comprehensive resource available on the topic of energy systems Presents an authoritative resource authored and edited by leading experts in the field Consolidates information currently scattered in publications from different research fields (engineering as well as physics, chemistry, environmental sciences and economics), thus ensuring a common standard and language

Author : Biplab Das,Ripon Patgiri,Valentina Emilia Balas
Publisher : Springer Nature
Page : 300 pages
File Size : 44,5 Mb
Release : 2022-08-31
Category : Technology & Engineering
ISBN : 9789811924125

Get Book

Advances in Smart Energy Systems by Biplab Das,Ripon Patgiri,Valentina Emilia Balas Pdf

This book discusses smart computing techniques which offer an effective solution for investigating and modeling the stochastic behavior of renewable energy generation, operation of grid-connected renewable energy systems, and smart decision-making among alternatives. It also discusses applications of soft computing techniques to make an intelligent decision for optimum use of suitable alternatives which gives an upper hand compared to conventional systems. It includes upgradation of the existing system by embedding of machine intelligence. The authors present combination of use of neutral networks, fuzzy systems, and genetic algorithms which are illustrated in several applications including forecasting, security, verification, diagnostics of a specific fault, efficiency optimization, etc. Smart energy systems integrate a holistic approach in diverse sectors including electricity, thermal comfort, power industry, transportation. It allows affordable and sustainable solutions to solve the future energy demands with suitable alternatives. Thus, contributions regarding integration of the machine intelligence with the energy system, for efficient collection and effective utilization of the available energy sources, are useful for further advanced studies.