This thesis introduces a fully data driven approach for the prediction and optimization of critical electrical grid states due to poor power quality. Therefore, a nonvolatile memory model for time series forecasting, designed to profit especially from big data bases and complex pattern use cases as well as an Artificial Intelligence based Smart Demand Side Management framework to enable system inherent resources / components for minimization of harmonic disturbances is applied to measured power grid scenarios.

This thesis introduces a fully data driven approach for the prediction and optimization of critical electrical grid states due to poor power quality. Therefore, a nonvolatile memory model for time series forecasting, designed to profit especially from big data bases and complex pattern use cases as well as an Artificial Intelligence based Smart Demand Side Management framework to enable system inherent resources / components for minimization of harmonic disturbances is applied to measured power grid scenarios.

This book provides a brief insight of various challenges and its mitigation techniques in microgrid due to power quality (PQ) issues. The central concept of this book revolves around the PQ issues in microgrid. The main objective of this book is to make aware of the power and control engineers with different innovative techniques to mitigate the challenges due to PQ issues in microgrid. The topics covered in this book are PQ disturbances in microgrid and different recent and innovative schemes to mitigate them. The book emphasizes technical issues, theoretical background, and practical applications that drive postgraduates, researchers, and practicing engineers with right advanced skills, vision, and knowledge in finding microgrid power quality issues, various technical challenges and providing mitigation techniques for the future sustainable microgrids.

In the era of propelling traditional energy systems to evolve towards smart energy systems, including power generation, energy storage systems, and electricity consumption have become more dynamic. The quality and reliability of power supply are impacted by the sporadic and rising use of electric vehicles, and domestic & industrial loads. Similarly, with the integration of solid state devices, renewable sources, and distributed generation, power generation processes are evolving in a variety of ways. Several cutting-edge technologies are necessary for the safe and secure operation of power systems in such a dynamic setting, including load distribution automation, energy regulation and control, and energy trading. This book covers the applications of various big data analytics, artificial intelligence, and machine learning technologies in smart grids for demand prediction, decision-making processes, policy, and energy management. The book delves into the new technologies such as the Internet of Things, blockchain, etc. for smart home solutions, and smart city solutions in depth in the context of the modern power systems. Technical topics discussed in the book include: • Hybrid smart energy system technologies • Energy demand forecasting • Use of different protocols and communication in smart energy systems • Power quality and allied issues and mitigation using AI • Intelligent transportation • Virtual power plants • AI business models.

This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.

This book addresses the principles and applications of metaheuristic approaches in engineering and related fields. The first part covers metaheuristics tools and techniques such as ant colony optimization and Tabu search, and their applications to several classes of optimization problems. In turn, the book’s second part focuses on a wide variety of metaheuristics applications in engineering and/or the applied sciences, e.g. in smart grids and renewable energy. In addition, the simulation codes for the problems discussed are included in an appendix for ready reference. Intended for researchers aspiring to learn and apply metaheuristic techniques, and gathering contributions by prominent experts in the field, the book offers readers an essential introduction to metaheuristics, its theoretical aspects and applications.

The text provides sustainable energy solutions using smart technologies such as artificial intelligence, blockchain technology, and the Internet of Things. It further presents several case studies on applications of the Internet of Things, artificial intelligence, and blockchain technology in the field of sustainable energy. Focuses on the integration of smart technology including artificial intelligence and sustainable energy Covers recent advancements in energy management techniques used in residential and commercial energy systems Highlights the use of artificial intelligence, machine learning, and their applications in sustainable energy Discusses important topics such as green energy, grid modernization, smart security in the power grid, and fault diagnosis Presents case studies on the applications of the Internet of Things, blockchain, and artificial intelligence in sustainable energy The text showcases the latest advancements, and the importance of technologies including artificial intelligence, blockchain, and Internet of Things in achieving sustainable energy systems. It further discusses the role of machine learning, applied deep learning, and edge computing in renewable energy. The text cover key concepts such as intelligent battery management system, energy trading, green energy, grid modernization, electric vehicles, and charging station optimization. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the fields including electrical engineering, electronics and communication engineering, computer engineering, and environmental engineering.