Bücher von Anuradha Tomar

This book provides an introduction to forecasting methods for renewable energy sources integrated with existing grid. It consists of two sections; the first one is on the generation side forecasting methods, while the second section deals with the different ways of load forecasting. It broadly includes artificial intelligence, machine learning, hybrid techniques and other state-of-the-art techniques for renewable energy and load predictions. The book reflects the state of the art in distributed generation system and future microgrids and covers theory, algorithms, simulations and case studies. It offers invaluable insights through this valuable resource to students and researchers working in the fields of renewable energy, integration of renewable energy with existing grid and electrical distribution network.

This book gathers selected papers presented at International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication (MARC 2021), held in Krishna Engineering College, Ghaziabad, India, during 10 - 11 December, 2021. This book discusses key concepts, challenges and potential solutions in connection with established and emerging topics in advanced computing, renewable energy and network communications.

This book provides an overview of how efficient building energy management can be done, considering the increasing importance of renewable energy integration. It also includes the grid-interactive building, their control, energy management, and optimization techniques to promote better understanding among researchers and business professionals in the utility sector and across industries. This book is written and edited by leading specialists active in concurrent developments in smart building management, renewable energy research, and application-driven R&D. The experiences and research work shared help the readers in enhancing their knowledge in the field of renewable energy, power engineering, building energy management, demand, and supply management and learn the technical analysis of the same in an insightful manner. Additionally, established and emerging applications related to applied areas like smart cities, the Internet of things, machine learning, artificial intelligence, etc., are developed and utilized to demonstrate recent innovations in smart building energy management.