The world population is continuously growing and reached a significant evolution of the society, where the number of people living in cities surpassed the number of people in rural areas. This puts national and local governments under pressure because the limited resources, such as water, electricity, and transports, must thus be optimized to cover the needs of the citizens. Therefore, different tools, from sensors to processes, service, and artificial intelligence, are used to coordinate the usage of infrastructures and assets of the cities to build the so called smart cities. Different definitions and theoretical models of smart cities are given in literature. However, smart city can usually be modelled by a layered architecture, where communication and networking layer plays a central role. In fact, smart city applications lay on collecting field data from different infrastructures and assets, processing these data, taking some intelligent control actions, and sharing information in a secure way. Thus, a two way reliable communications layer is the basis of smart cities. This chapter introduces the basic concepts of this field and focuses on the role of communication technologies in smart cities. Potential technologies for smart cities are discussed, especially the recent wireless technologies adapted to smart city requirements.
Part of the book: Smart Cities Technologies
This chapter delves into the realm of “Big Data and Analytics in Smart Grid”, focusing specifically on the domain of forecasting energy consumption in educational institution buildings. The chapter starts with a high point of smart grid and forecasting electrical energy consumption in several areas and then describes the forecasting models in buildings for educational institutions. The study gives an overview of forecasting models in this kind of prediction, and it gives their potential and classification based on extensive studies and research. The chapter unfolds the practical advantages and challenges that big data offers to optimize energy forecasting for educational institutions. The exploration covers the entire big data pipeline in smart grid, including data selection, preparation, and the crucial phases of training and testing.
Part of the book: ICT for Smart Grid - Recent Advances, New Perspectives, and Applications [Working title]