Название: Smart Cities: Big Data Prediction Methods and Applications
Автор: Hui Liu
Издательство: Springer
Год: 2020
Страниц: 338
Язык: английский
Формат: pdf (true)
Размер: 16.7 MB

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.

With the continuous updating and development of information technology, a new generation of information technology such as the Internet of Things, the Internet, and cloud computing has gradually entered the field of vision. Smart city is a new urban development and construction model developed on information technology support systems. It provides new ideas and methods for solving a series of problems brought by the rapid development of urbanization. This book briefly introduces several key issues and related technologies of the smart city. It describes the origins, implications, and developments of smart cities around the world, and focuses on smart grid and buildings, smart traffic systems, and smart environment to analyze the important impact of smart cities on future urban development. The necessity of research on the relevant content is fully analyzed. At the same time, it also briefly introduces the relevant technical framework for implementing each issue.