Advances on Smart Cities and Smart Buildings

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		Michele Roccotelli	+ 1479 word(s)	1479	2022-01-11 04:04:04	\|
2	format correct	Bruce Ren	Meta information modification	1479	2022-01-25 02:23:21	\|

This entry is adapted from the peer-reviewed paper 10.3390/app12020631

Modern cities are facing the challenge of combining competitiveness on a global city scale and sustainable urban development to become smart cities. A smart city is a high-tech intensive and advanced city that connects people, information, and city elements using new technologies in order to create a sustainable, greener city; competitive and innovative commerce; and an increased quality of life. This Special Issue collects the recent advancements on smart cities and covers different topics and aspects.

Smart Cities smart buildings smart mobility smart grids

1. Background

Technological innovations have revolutionized the lifestyle of society and have led to the development of advanced and intelligent cities. The term smart city has recently become synonymous for a city that is characterized by an intelligent and extensive use of Information and Communications Technologies (ICTs) in order to allow the efficient use of information. In this context, new solutions and tools are offered to tourists to optimize and customize itinerary planning. In particular, graph theory and optimization algorithms are used to find the optimal touristic itinerary paths and, a multi-algorithms strategy is used to maximize the number of attractions (PoIs) to be visited on these paths ^[1].

A review of the sensors deployed in a smart city is conducted in ^[2], both from a technological and functional point of view. Sensors play an important role, as they gather relevant information from the city, citizens, and the corresponding communication networks and transfer the information in real-time. Although the use of these sensors is diverse, their application can be categorized in six different groups: energy, health, mobility, security, water, and waste management. Based on these groups, this review presents an analysis of different sensors that are typically used in efforts toward creating smart cities. Insights about different applications and communication systems are provided as well as the main opportunities and challenges faced when making a transition to a smart city. Ultimately, this process is not only about smart urban infrastructure, but more importantly, it is about how these new sensing capabilities and digitalization developments improve the quality of life of the citizens who live in these cities.

2. Advances on Smart Cities and Smart Buildings

In addition, the global decarbonization and electrification of the world’s energy demands has led to the quick adoption of Electric Vehicle (EV) technology. There is an emerging need to provide a wide network of fast Vehicle-to-Grid (V2G) charging stations to satisfy the energy demand and to guarantee the sufficient autonomy of such vehicles. Accordingly, V2G charging stations must be prepared to work properly with every manufacturer and operator and to provide reliable designs and validation processes. To support such processes, the development of power electric vehicle emulators with V2G capability is essential ^[3]. In ^[3], the design and development details of an electric vehicle emulator for testing V2G chargers with power factor grid correction functionality are provided, and the ability to emulate a real V2G EV to handle fast charge/discharge with an EV charger is validated experimentally. Another solution to support the attractiveness of EVs by enhancing the autonomy of batteries and by limiting the range anxiety is provided by the analysis in ^[4]. The integration of an auxiliary power unit (APU) can extend the range of a vehicle, making them more attractive to consumers. Recently, many extended-range electric vehicle systems and configurations have been proposed to recover energy. However, an extensive analysis of the most relevant technologies that recover energy, the current topologies and configurations of such devices, and the state-of-the-art of control methods used to manage energy is necessary to identify the best solution. The analysis presented mainly focuses on finding maximum fuel economy, reducing emissions, minimizing the system’s costs, and providing optimal driving performance. The evaluation of range extenders for electric vehicles aims to guide researchers and car manufactures to generate new topologies and configurations for EVs with optimized range, improved functionality, and low emissions.

The problem of forecasting the electricity demand is not only linked to EVs. Today, buildings are still the main contributors of energy consumption within a smart city. In particular, the long-term electricity demand forecast is essential for the energy provider to analyze the future demand and for the accurate management of the demand response. Forecasting the consumer electricity demand with efficient and accurate strategies will help the energy provider to optimally plan generation points, such as solar and wind, and to produce energy accordingly to reduce the rate of depletion. An efficient and accurate forecasting model is provided by ^[5] to study the daily consumption of the consumers from their historical data and to forecast the necessary energy demand from the consumer’s side. The proposed recurrent neural network gradient boosting regression tree (RNN-GBRT) forecasting technique allows the demand for electricity to be reduced by studying the daily usage patterns of consumers. The efficiency of the proposed forecasting model is compared with various conventional models.

The prediction methods are also adopted in vehicle sharing systems. In this framework, the work in ^[6] deals with the long-term prediction of bike rental/drop-off demands at given bike station locations in the expansion areas. The real-world bike stations are mainly built-in batches for expansion areas. To address the problem, they propose LDA (Long-Term Demand Advisor), a framework that can be used to estimate the long-term characteristics of newly established stations. In LDA, several engineering strategies are proposed to extract discriminative and representative features for long-term demands. Moreover, for the original and newly established stations, several feature extraction methods and an algorithm are proposed to model the correlations between urban dynamics and long-term demands. Real-world data from New York City’s bike-sharing system are evaluated to show that the LDA framework outperforms baseline approaches.

Furthermore, ref. ^[7] proposes an analysis of European cycle logistics projects, studying how the corresponding supporting policies have an impact on their economic performance in terms of profit and profitability. First, they identify project success factors by geographic area and project-specific characteristics; then, they statistically test possible dependence relationships with supporting policies and economic results. Moreover, they provide a value-based identification of those characteristics and policies that more commonly lead to better economic results. The work can serve as a basis for the prioritization and contextualization of those project functionalities and public policies to be implemented in a European context. It was determined that cycle logistics projects in Europe achieve high profit and profitability levels and that the current policies are generally working well and the support of them as well as their profit and profitability vary across the bike model utilized: mixing cargo bikes and tricycles generates the highest profit and profitability, whilst a trailer–tricycle–cargo bike mix paves the way for high volumes and market shares.

Some real-use smart city case studies are presented in ^[8]^[9]. For instance, in ^[8], the Campus City project is presented. In the Campus City initiative, the Challenge Living Lab platform is used to promote research, innovation, and entrepreneurship, with the intention of creating urban infrastructure and creative talent (human resources) that solves different community, industrial, and government Pain Points within a Smart City ecosystem. The main contribution is the presentation of a working model and the open innovation ecosystem used in Tecnologico de Monterrey that could be used as both a learning mechanism as well as a base model for scaling it up into a Smart Campus and Smart City. A discussion on the findings of the model and challenge implementation is provided, showing that the Campus City initiative and the Challenge Living Lab allow the identification of highly relevant and meaningful challenges while providing a pedagogic framework in which students are highly motivated, engaged, and prepared to tackle different problems that involve government, community, industry, and academia.

Scaling up the analysis, another practical example is given by the City of Monterrey, Mexico, which is presented at two planning scales ^[9]: at the metropolitan and local levels. Both scales of analysis measure accessibility to main destinations using walking and cycling as the main transport modes. The results demonstrate that the levels of accessibility at the metropolitan level are divergent, depending on the desired destination as well as on the planning processes (both formal and informal) from different areas of the city. At the local level, the Distrito Tec Area is diagnosed in terms of accessibility to assess to what extent it can be considered a part of a 15 min city. The results show that Distrito Tec lacks the desired parameters of accessibility to all destinations to be a 15 min city. Nevertheless, there is a considerable increase in accessibility levels when cycling is used as the main mode of transportation. The current research project serves as an initial approach to understand the accessibility challenges of the city at different planning levels by generating useful and disaggregated data. Finally, it concludes by providing general recommendations that should be considered during planning processes that are aimed at improving accessibility and sustainability.

References

Mangini, A.M.; Roccotelli, M.; Rinaldi, A. A Novel Application Based on a Heuristic Approach for Planning Itineraries of One-Day Tourist. Appl. Sci. 2021, 11, 8989.
Ramírez-Moreno, M.A.; Keshtkar, S.; Padilla-Reyes, D.A.; Ramos-López, E.; García-Martínez, M.; Hernández-Luna, M.C.; Mogro, A.E.; Mahlknecht, J.; Huertas, J.I.; Peimbert-García, R.E.; et al. Sensors for Sustainable Smart Cities: A Review. Appl. Sci. 2021, 11, 8198.
García-Martínez, E.; Muñoz-Cruzado-Alba, J.; Sanz-Osorio, J.F.; Perié, J.M. Design and Experimental Validation of Power Electric Vehicle Emulator for Testing Electric Vehicle Supply Equipment (EVSE) with Vehicle-to-Grid (V2G) Capability. Appl. Sci. 2021, 11, 11496.
Puma-Benavides, D.S.; Izquierdo-Reyes, J.; Calderon-Najera, J.D.D.; Ramirez-Mendoza, R.A. A Systematic Review of Technologies, Control Methods, and Optimization for Extended-Range Electric Vehicles. Appl. Sci. 2021, 11, 7095.
Dash, S.K.; Roccotelli, M.; Khansama, R.R.; Fanti, M.P.; Mangini, A.M. Long Term Household Electricity Demand Forecasting Based on RNN-GBRT Model and a Novel Energy Theft Detection Method. Appl. Sci. 2021, 11, 8612.
Hsieh, H.-P.; Lin, F.; Jiang, J.; Kuo, T.-Y.; Chang, Y.-E. Inferring Long-Term Demand of Newly Established Stations for Expansion Areas in Bike Sharing System. Appl. Sci. 2021, 11, 6748.
Giglio, C.; Musmanno, R.; Palmieri, R. Cycle Logistics Projects in Europe: Intertwining Bike-Related Success Factors and Region-Specific Public Policies with Economic Results. Appl. Sci. 2021, 11, 1578.
Huertas, J.I.; Mahlknecht, J.; Lozoya-Santos, J.D.J.; Uribe, S.; López-Guajardo, E.A.; Ramirez-Mendoza, R.A. Campus City Project: Challenge Living Lab for Smart Cities. Appl. Sci. 2021, 11, 11085.
Gaxiola-Beltrán, A.L.; Narezo-Balzaretti, J.; Ramírez-Moreno, M.A.; Pérez-Henríquez, B.L.; Ramírez-Mendoza, R.A.; Krajzewicz, D.; Lozoya-Santos, J.D.-J. Assessing Urban Accessibility in Monterrey, Mexico: A Transferable Approach to Evaluate Access to Main Destinations at the Metropolitan and Local Levels. Appl. Sci. 2021, 11, 7519.

© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.

Upload a video for this entry

Information

Subjects: Energy & Fuels; Transportation Science & Technology; Engineering, Electrical & Electronic

Contributor MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :

Michele Roccotelli

View Times: 637

Update Date: 25 Jan 2022

Table of Contents

Video Upload Options

Confirm

1. Background

2. Advances on Smart Cities and Smart Buildings

References