Smart Cities: Big Data Prediction Methods and Applications, 1st ed. 2020

Smart Cities: Big Data Prediction Methods and Applications is the first reference to provide a comprehensive overview of smart cities with the latest big data predicting techniques.

 This timely book discusses big data forecasting for smart cities. It introduces big data forecasting techniques for the key aspects (e.g., traffic, environment, building energy, green grid, etc.) of smart cities, and explores three key areas that can be improved using big data prediction: grid energy, road traffic networks and environmental health in smart cities. The big data prediction methods proposed in this book are highly significant in terms of the planning, construction, management, control and development of green and smart cities.

 Including numerous case studies to explain each method and model, this easy-to-understand book appeals to scientists, engineers, college students, postgraduates, teachers and managers from various fields of artificial intelligence, smart cities, smart grid, intelligent traffic systems, intelligent environments and big data computing.

Prof. Dr. -Ing. habil. Hui Liu is a Full Professor of Artificial Intelligence & Smart Cities at the Central South University, China. He is Deputy Dean of the Faculty of Traffic and Transportation Engineering, Director of the Institute of Artificial Intelligence and Robotics and a member of various academic committees at Central South University. He previously served as the BMBF junior group leader appointed by the Ministry of Education & Research of Germany at University of Rostock, Germany.

Broadens readers' understanding of the smart cities

Describes in detail the latest theories and specific applications of smart time series prediction methods in smart cities, as well as a big data framework for three key aspects of building smart cities: smart grid, smart traffic systems and smart environments

Summarizes the most important big data forecasting theories and techniques for building smart cities and proposes embedded prediction algorithms and methods for the mainstream big data platforms