Download or read book Machine Learning Algorithms and Applications for Sustainable Smart Grid written by Di Wu and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Smart grid is a complex electrical power network comprising different subsystems with alevel of automation enabling the use of renewable energy while maintaining the grid stability and affordability of the energy. With the increasing attention on environment protection and development of sensors, communication, and computation tools, the smart grid concepthas gained a fast development in recent years. It could significantly improve energy efficiency, allow deep decarbonization and protect the environment. Machine learning is of essential importance to enable intelligent power systems. In this thesis, we use three pieces of work to demonstrate how the smart grid can benefit from machine learning algorithms. First, we note that workplace electric vehicle(EV) charging is now supported by more and more companies to encourage EV adoption which is environmentally friendly. In the meantime, renewable energies are becoming animportant power source. We propose to address the challenges of energy management in office buildings integrated with photovoltaic (PV) systems and workplace EV charging with a stochastic programming framework. Two computationally efficient control algorithms,Stochastic Programming and Load forecasting for Energy management with Two stages(SPLET) and Sample Average Approximation based SPLET (SAA SPLET) are proposed. Secondly, accurate electricity load forecasting is of crucial importance for power system operation and smart grid energy management. Multiple kernel learning (MKL) is suitable for electricity load forecasting, because this type of method provides more flexibility than traditional kernel methods. However, conventional MKL methods usually lead to complex optimization problems. At the scale of residential homes, another important aspect of this application is that there may be very little data available to train a reliable forecasting model for a new home, while at the same time we may have prior knowledge learned from other homes. In particular, we first adopt boosting to learn an ensemble of multiple kernel regressors, and then we further extend this framework to the context of transfer learning when limited data is available for target homes. Finally, we aim to tackle home energy management without knowing the system dynamics. We propose to formalize home energy management, including buying energy from or selling energy back to the power grid and EV charging scheduling as a Markov Decision Process (MDP) and propose two model-free reinforcement learning based control algorithms to address it. The objective for the proposed algorithms is to minimize the long-term operating cost. Simulation results are presented with real-world data and show that the proposed algorithms can significantly reduce the electricity cost as well as peak power consumptions of the home." --

Download or read book Artificial Intelligence for Smart and Sustainable Energy Systems and Applications written by Miltiadis D. Lytras and published by MDPI. This book was released on 2020-05-27 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Energy has been a crucial element for human beings and sustainable development. The issues of global warming and non-green energy have yet to be resolved. This book is a collection of twelve articles that provide strong evidence for the success of artificial intelligence deployment in energy research, particularly research devoted to non-intrusive load monitoring, network, and grid, as well as other emerging topics. The presented artificial intelligence algorithms may provide insight into how to apply similar approaches, subject to fine-tuning and customization, to other unexplored energy research. The ultimate goal is to fully apply artificial intelligence to the energy sector. This book may serve as a guide for professionals, researchers, and data scientists—namely, how to share opinions and exchange ideas so as to facilitate a better fusion of energy, academic, and industry research, and improve in the quality of people's daily life activities.

Download or read book Machine Learning for Sustainable Development written by Kamal Kant Hiran and published by Walter de Gruyter GmbH & Co KG. This book was released on 2021-07-19 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book will focus on the applications of machine learning for sustainable development. Machine learning (ML) is an emerging technique whose diffusion and adoption in various sectors (such as energy, agriculture, internet of things, infrastructure) will be of enormous benefit. The state of the art of machine learning models is most useful for forecasting and prediction of various sectors for sustainable development.

Download or read book Intelligent Learning Approaches for Renewable and Sustainable Energy written by Josep M. Guerrero and published by Elsevier. This book was released on 2024-02-21 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent Learning Approaches for Renewable and Sustainable Energy provides a practical, systematic overview of the application of advanced intelligent control techniques, adaptive techniques, machine learning algorithms, and predictive control in renewable and sustainable energy.The book begins by introducing the intelligent learning approaches, and the roles of artificial intelligence and machine learning in terms of energy and sustainability, grid transformation, large-scale integration of renewable energy, and variability and flexibility of renewable sources. The second section of the book provides detailed coverage of intelligent learning techniques as applied to key areas of renewable and sustainable energy, including forecasting, supply and demand, integration, energy management, and optimization, supported by case studies, figures, schematics, and references.This is a useful resource for researchers, scientists, advanced students, energy engineers, R&D professionals, and other industrial personnel with an interest in sustainable energy and integration of renewable energy sources, energy systems, energy engineering, machine learning, and artificial intelligence. Explores cutting-edge intelligent techniques and their implications for future energy systems development Opens the door to a range of applications across forecasting, supply and demand, energy management, optimization, and more Includes a range of case studies that provide insights into the challenges and solutions in real-world applications

Download or read book Green Machine Learning and Big Data for Smart Grids written by V. Indragandhi and published by Elsevier. This book was released on 2024-11-01 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Green Machine Learning and Big Data for Smart Grids: Practices and Applications is a guidebook to the best practices and potential for green data analytics when generating innovative solutions to renewable energy integration in the power grid. This book begins with a solid foundation in the concept of "green” machine learning and the essential technologies for utilising data analytics in smart grids. A variety of scenarios are examined closely, demonstrating the opportunities for supporting renewable energy integration using machine learning, from forecasting and stability prediction to smart metering and disturbance tests. Uses for control of physical components including inverters and converters are examined, along with policy implications. Importantly, real-world case studies and chapter objectives are combined to signpost essential information, and to support understanding and implementation. Part of the cutting-edge series 'Advances in Intelligent Energy Systems', 'Green Machine Learning and Big Data for Smart Grids' provides researchers, students, and industry practitioners with an understanding of the complex interactions and opportunities between data science and sustainable energy systems.

Download or read book Smart Grid 3 0 written by Bhargav Appasani and published by Springer Nature. This book was released on 2023-10-15 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first on Smart Grid 3.0. The book presents literature reviews of recent computational and communication technologies and their application in the evolution of smart grids to Smart Grid 3.0. It offers new control solutions, architectures and energy management strategies that are based on artificial intelligence and deep learning techniques. The book details the hardware and software implementation of fault identification or detection based on synchrophasor data and machine learning. It also discusses blockchain architectures for smart grid applications such as electric vehicles, home automation and automatic metering infrastructure.

Download or read book Applications of Deep Machine Learning in Future Energy Systems written by Mohammad-Hassan Khooban and published by Elsevier. This book was released on 2024-08-20 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applications of Deep Machine Learning in Future Energy Systems pushes the limits of current Artificial Intelligence techniques to present deep machine learning suitable for the complexity of sustainable energy systems. The first two chapters take the reader through the latest trends in power engineering and system design and operation, before laying out the current AI approaches and our outstanding limitations. Later chapters provide in-depth accounts of specific challenges and the use of innovative third-generation machine learning, including neuromorphic computing, to resolve issues from security to power supply. An essential tool for the management, control, and modelling of future energy systems, Applications of Deep Machine Learning maps a practical path towards AI capable of supporting sustainable energy. Clarifies the current state and future trends of energy system machine learning and the pitfalls facing our transitioning systems Provides guidance on 3rd-generation AI tools for meeting the challenges of modeling and control in modern energy systems Includes case studies and practical examples of potential applications to inspire and inform researchers and industry developers

Download or read book Intelligent Renewable Energy Systems written by Neeraj Priyadarshi and published by John Wiley & Sons. This book was released on 2022-01-19 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: INTELLIGENT RENEWABLE ENERGY SYSTEMS This collection of papers on artificial intelligence and other methods for improving renewable energy systems, written by industry experts, is a reflection of the state of the art, a must-have for engineers, maintenance personnel, students, and anyone else wanting to stay abreast with current energy systems concepts and technology. Renewable energy is one of the most important subjects being studied, researched, and advanced in today’s world. From a macro level, like the stabilization of the entire world’s economy, to the micro level, like how you are going to heat or cool your home tonight, energy, specifically renewable energy, is on the forefront of the discussion. This book illustrates modelling, simulation, design and control of renewable energy systems employed with recent artificial intelligence (AI) and optimization techniques for performance enhancement. Current renewable energy sources have less power conversion efficiency because of its intermittent and fluctuating behavior. Therefore, in this regard, the recent AI and optimization techniques are able to deal with data ambiguity, noise, imprecision, and nonlinear behavior of renewable energy sources more efficiently compared to classical soft computing techniques. This book provides an extensive analysis of recent state of the art AI and optimization techniques applied to green energy systems. Subsequently, researchers, industry persons, undergraduate and graduate students involved in green energy will greatly benefit from this comprehensive volume, a must-have for any library. Audience Engineers, scientists, managers, researchers, students, and other professionals working in the field of renewable energy.

Download or read book IoT and Analytics in Renewable Energy Systems Volume 1 written by O.V. Gnana Swathika and published by CRC Press. This book was released on 2023-08-11 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: Smart grid technologies include sensing and measurement technologies, advanced components aided with communications and control methods along with improved interfaces and decision support systems. Smart grid techniques support the extensive inclusion of clean renewable generation in power systems. Smart grid use also promotes energy saving in power systems. Cyber security objectives for the smart grid are availability, integrity and confidentiality. Five salient features of this book are as follows: AI and IoT in improving resilience of smart energy infrastructure IoT, smart grids and renewable energy: an economic approach AI and ML towards sustainable solar energy Electrical vehicles and smart grid Intelligent condition monitoring for solar and wind energy systems

Download or read book Intelligent Data Analytics for Condition Monitoring written by Hasmat Malik and published by Academic Press. This book was released on 2021-02-24 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent Data-Analytics for Condition Monitoring: Smart Grid Applications looks at intelligent and meaningful uses of data required for an optimized, efficient engineering processes. In addition, the book provides application perspectives of various deep learning models for the condition monitoring of electrical equipment. With chapters discussing the fundamentals of machine learning and data analytics, the book is divided into two parts, including i) The application of intelligent data analytics in Solar PV fault diagnostics, transformer health monitoring and faults diagnostics, and induction motor faults and ii) Forecasting issues using data analytics which looks at global solar radiation forecasting, wind data forecasting, and more. This reference is useful for all engineers and researchers who need preliminary knowledge on data analytics fundamentals and the working methodologies and architecture of smart grid systems. Features deep learning methodologies in smart grid deployment and maintenance applications Includes coding for intelligent data analytics for each application Covers advanced problems and solutions of smart grids using advance data analytic techniques

Download or read book Advances in Artificial Intelligence Application in Data Analysis and Control of Smart Grid written by Xin Ning and published by Frontiers Media SA. This book was released on 2023-11-23 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: Smart grid (SG) is considered a form of intelligent system that allows the electric grid to perform its functions efficiently. The SG is a network that allows for the flow of electrical energy and data, where the data is used to make intelligent decisions in the operation of the electric grid. Artificial intelligence (AI) techniques, such as expert system (ES), Machine Learning (ML), and deep Learning (DL) have brought an advancing frontier in power electronics and power engineering with their powerful data processing capabilities. The SG relies on the flow of data to make its intelligent control; therefore, AI technology is a perfect fit for the SG. The application of AI technology in the SG has the potential to improve the intelligence of the SG. This research topic is focused on ways of improving the data analysis and control of SG by leveraging technologies. Manuscripts with the progress made in solving a range of miscellaneous and critical problems in SG by leveraging AI methods such as ES, ML, and DL methods are welcome. Reviews and original research that describe the latest developments in this field are considered for publication in this research topic. The scope of this Research Topic will include the following themes, but are not limited to: 1. Data-driven and artificial intelligence approaches to enhancing flexibility and resilience of SG. 2. Expert system, Machine Learning and Deep Learning, reinforcement learning and transfer learning for applications in SG. 3. AI for development in ensuring high reliability and stability of electric power system with high penetration of renewable energy. 4. AI for studies in operation protection, integrated planning, and control of SG systems. 5. AI for development in diagnostics and diagnostics for SG. 6. Health monitoring of a modern wind generation system using an adaptive neuro-fuzzy system. 7. Space vector fault pattern identification of a smart grid subsystem by neural mapping. 8. Control techniques, mathematical programming methods, optimization techniques and metaheuristics applied in SG. 9. AI and optimization techniques for green energy and carbon footprint. 10. Novel applications of AI-based smart grids in smart cities, smart transportation, smart healthcare, and smart manufacturing.

Download or read book Applications of AI and IOT in Renewable Energy written by Rabindra Nath Shaw and published by Academic Press. This book was released on 2022-02-09 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applications of AI and IOT in Renewable Energy provides a future vision of unexplored areas and applications for Artificial Intelligence and Internet of Things in sustainable energy systems. The ideas presented in this book are backed up by original, unpublished technical research results covering topics like smart solar energy systems, intelligent dc motors and energy efficiency study of electric vehicles. In all these areas and more, applications of artificial intelligence methods, including artificial neural networks, genetic algorithms, fuzzy logic and a combination of the above in hybrid systems are included. This book is designed to assist with developing low cost, smart and efficient solutions for renewable energy systems and is intended for researchers, academics and industrial communities engaged in the study and performance prediction of renewable energy systems. Includes future applications of AI and IOT in renewable energy Based on case studies to give each chapter real-life context Provides advances in renewable energy using AI and IOT with technical detail and data

Download or read book Sustainable Development in AI Blockchain and E Governance Applications written by Kumar, Rajeev and published by IGI Global. This book was released on 2024-02-09 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the age of immediate technical expansion, our world faces a multifaceted challenge: ensuring the sustainability of our digital transformation. Governments and organizations have wholeheartedly embraced innovative technologies such as artificial intelligence, blockchain, and e-governance, but in doing so, they have encountered a complex web of issues. These range from cybersecurity concerns in an increasingly digitalized world to the need for intelligent systems capable of managing automation infrastructure and interconnected environments. Sustainable Development in AI, Blockchain, and E-Governance Applications offers a forward-thinking approach that harnesses the synergy between intelligent systems, machine learning, deep learning, and blockchain methods. It explores data-driven decision-making, automation infrastructure, autonomous transportation, and the creation of connected buildings, all aimed at crafting a sustainable digital future. By delving into topics like machine learning for smart parking, disease classification through neural networks, and the Internet of Things (IoT) for smarter cities, this book equips academic scholars with the tools they need to navigate the complex terrain of technology and governance. Academic scholars and researchers in technology, governance, and sustainability will find this book to be an indispensable resource. It caters to those seeking a comprehensive understanding of current and future trends in the integration of intelligent systems with cybersecurity applications.

Download or read book Artificial Intelligence Enabled Computational Methods for Smart Grid Forecast and Dispatch written by Yuanzheng Li and published by Springer Nature. This book was released on 2023-05-05 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the increasing penetration of renewable energy and distributed energy resources, smart grid is facing great challenges, which could be divided into two categories. On the one hand, the endogenous uncertainties of renewable energy and electricity load lead to great difficulties in smart grid forecast. On the other hand, massive electric devices as well as their complex constraint relationships bring about significant difficulties in smart grid dispatch. Owe to the rapid development of artificial intelligence in recent years, several artificial intelligence enabled computational methods have been successfully applied in the smart grid and achieved good performances. Therefore, this book is concerned with the research on the key issues of artificial intelligence enabled computational methods for smart grid forecast and dispatch, which consist of three main parts. (1) Introduction for smart grid forecast and dispatch, in inclusion of reviewing previous contribution of various research methods as well as their drawbacks to analyze characteristics of smart grid forecast and dispatch. (2) Artificial intelligence enabled computational methods for smart grid forecast problems, which are devoted to present the recent approaches of deep learning and machine learning as well as their successful applications in smart grid forecast. (3) Artificial intelligence enabled computational methods for smart grid dispatch problems, consisting of edge-cutting intelligent decision-making approaches, which help determine the optimal solution of smart grid dispatch. The book is useful for university researchers, engineers, and graduate students in electrical engineering and computer science who wish to learn the core principles, methods, algorithms, and applications of artificial intelligence enabled computational methods.

Download or read book Sustainable Networks in Smart Grid written by B.D. Deebak and published by Academic Press. This book was released on 2022-03-26 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sustainable Networks in Smart Grid presents global challenges in smart metering with renewable energy resources, micro-grid design, communication technologies, big data, privacy and security in the smart grid. Providing an overview of different available PLC technologies and configurations and their applications in different sectors, this book provides case studies and practical implementation details of smart grid technology, paying special attention to Advanced Metering Infrastructure (AMI) scenarios with the presence of Distribution Grid (DG) and Electric Vehicles (EV). Covering regulatory policies for energy storage, management strategies for microgrid operation, and key performance indicators for smart grid development, this reference compiles up-to-date information on different aspects of the Internet of Smart Metering. In addition, innovative contributions on Data Analytics, Energy Theft Detection, Data-Driven Framework, Blockchain–IoT-enabled Sensor Networks, and Smart Contacts in the Blockchain are also included. Includes case studies and practical implementation examples of different smart grid applications, their benefits, characteristics and requirements Provides a SWOT analysis of the impact of recent regulatory changes on the business case for energy storage (ES) Presents a comprehensive survey of privacy-preserving schemes for smart grid communications

Download or read book Machine Learning and Data Science in the Power Generation Industry written by Patrick Bangert and published by Elsevier. This book was released on 2021-01-14 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning and Data Science in the Power Generation Industry explores current best practices and quantifies the value-add in developing data-oriented computational programs in the power industry, with a particular focus on thoughtfully chosen real-world case studies. It provides a set of realistic pathways for organizations seeking to develop machine learning methods, with a discussion on data selection and curation as well as organizational implementation in terms of staffing and continuing operationalization. It articulates a body of case study–driven best practices, including renewable energy sources, the smart grid, and the finances around spot markets, and forecasting. Provides best practices on how to design and set up ML projects in power systems, including all nontechnological aspects necessary to be successful Explores implementation pathways, explaining key ML algorithms and approaches as well as the choices that must be made, how to make them, what outcomes may be expected, and how the data must be prepared for them Determines the specific data needs for the collection, processing, and operationalization of data within machine learning algorithms for power systems Accompanied by numerous supporting real-world case studies, providing practical evidence of both best practices and potential pitfalls

Download or read book Artificial Intelligence Machine Learning and Optimization Tools for Smart Cities written by Panos M. Pardalos and published by Springer Nature. This book was released on 2022-01-09 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume offers a wealth of interdisciplinary approaches to artificial intelligence, machine learning and optimization tools, which contribute to the optimization of urban features towards forming smart, sustainable, and livable future cities. Special features include: New research on the design of city elements and smart systems with respect to new technologies and scientific thinking Discussions on the theoretical background that lead to smart cities for the future New technologies and principles of research that can promote ideas of artificial intelligence and machine learning in optimized urban environments The book engages students and researchers in the subjects of artificial intelligence, machine learning, and optimization tools in smart sustainable cities as eminent international experts contribute their research results and thinking in its chapters. Overall, its audience can benefit from a variety of disciplines including, architecture, engineering, physics, mathematics, computer science, and related fields.