دانلود کتاب هوش مصنوعی و دیجیتالی شدن در مدیریت انرژی

As energy systems grow in complexity due to the increasing integration of renewable energy and the electrification of transportation, artificial intelligence (AI) and digitalization have arisen as the prime technologies to address emerging challenges. Machine learning, multi-agent simulations, optimization, and game theory methods are being successfully applied to a number of energy management tasks with the support of robotics, Internet of Things (IoT), and cloud/edge computing technologies. The goal of this book is to provide a venue for a deep dive into the AI and digitalization ecosystem for energy management through a review of the state-of-the-art and the identification of future directions. The book is structured in two parts. The first part focuses on the fundamentals of AI and digitalization in energy management, while the second part provides a review of representative applications. Energy management emerged as a distinct discipline after the oil crisis of the 1970s. The initial focus was on energy conservation in reaction to rising oil prices, but the endeavor gradually evolved as a more global and systematic approach to achieving economic sustainability in energy by ensuring economic competitiveness through the provision of efficient, resilient, and secure services that adhere to environmental standards to reduce air and water pollution. With the introduction of renewable energy as a growing component of power systems, energy management has been used to address the variability and intermittency of solar and wind power generation. Modern energy management covers a wide range of applications from wide area monitoring and control to home energy management and anything in between. Accordingly, the use of the term energy management in this book reflects its broader application to qualify any system whose function is to monitor and control electricity networks that integrate renewable energy, energy storage, electric vehicles, and demand response to optimize and secure energy use. As shown in Figure 1.1, energy management systems can cover global power grids as well as local units of varying sizes, such as microgrids and nanogrids.