Advances in Digitalization and Machine Learning for Integrated Building-Transportation Energy Systems PDF Download
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Author: Yuekuan Zhou
Publisher: Elsevier
ISBN: 0443131783
Category : Computers
Languages : en
Pages : 302
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Book Description
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: Yuekuan Zhou
Publisher: Elsevier
ISBN: 0443131783
Category : Computers
Languages : en
Pages : 302
Get Book
Book Description
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
Publisher: IGI Global
ISBN:
Category : Technology & Engineering
Languages : en
Pages : 351
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Book Description
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
ISBN: 3039433822
Category : Technology & Engineering
Languages : en
Pages : 272
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Book Description
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
Publisher: Academic Press
ISBN: 0323914284
Category : Science
Languages : en
Pages : 418
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Book Description
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: O.V. Gnana Swathika
Publisher: CRC Press
ISBN: 1000537943
Category : Technology & Engineering
Languages : en
Pages : 421
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Book Description
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:
ISBN: 9783039433834
Category :
Languages : en
Pages : 272
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Book Description
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: Biplab Das
Publisher: Springer Nature
ISBN: 9811924120
Category : Technology & Engineering
Languages : en
Pages : 300
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Book Description
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.
Author: Ajay Kumar Vyas
Publisher: John Wiley & Sons
ISBN: 1119761697
Category : Computers
Languages : en
Pages : 276
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Book Description
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: S. Vijayalakshmi
Publisher: Springer Nature
ISBN: 3031150449
Category : Technology & Engineering
Languages : en
Pages : 318
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Book Description
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: Mustapha Hatti
Publisher: Springer
ISBN: 9783031212154
Category : Technology & Engineering
Languages : en
Pages : 0
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Book Description
In this book, one hundred selected articles, in which the technology and science elite share, contribute to technology development, collaborate and evolve the latest cutting-edge technologies, open ecosystem resources, new innovative computing solutions, hands-on labs and tutorials, networking and community building, to ensure better integration of artificial intelligence into renewable energy systems. Innovation in computing continues at a growing pace. The key to success in this area is not only hardware, but also the ability to leverage rapid advances in artificial intelligence (including machine learning and deep learning), data analytics, data streaming, and cloud computing, which go hand in hand with intensive research activity on the underlying computational methods. The chapters in this book are organized into thematic sections on: advanced computing techniques; artificial intelligence; smart and sustainable cities; renewable energy systems; materials in renewable energy; smart energy efficiency; smart cities applications: recent developments and new trends; online, supervision of renewable energy platforms; predictive control in renewable systems; smart embedded systems for photovoltaic applications.
