The Smart City, coordinated development of economy, ecology and society, is the future of cities. Urban renewal is an important way to build the Smart City. Based on the Smart City theory, the authors propose the “12345” model of urban renewal, which means developing a tailored proposal of the city and citizen, and building “one city management system, two supporting safeguard measures, three information infrastructure platforms, four urban management modes, and five application service systems.” The city will gain the ability of self-adjustment and self-improvement. Smart urban renewal model not only solves the problems of the city at this stage, but also promotes the all-round development of the city, and access to sustainable development.

Green Smart City (GSC), is one of the new types of cities which integrates eco-concepts and information technologies. As GSC construction is a resource intensive process, it requires a large amount of knowledge, information, talent and capital. Thus, setting up an innovation ecosystem is an astute way to promote the construction of a GSC. This paper, based on the case study by the Insigma Group, tries to explore the rules of innovation ecosystem building for the construction of GSCs in China.

Buildings contribute to a major part of energy consumption in urban areas, especially in areas like Hong Kong which is full of high-rise buildings. Smart buildings with high efficiency can reduce the energy consumption largely and help achieve green cities or smart cities. Design and control optimization of building energy systems therefore plays a significant role to obtain the optimal performance. This paper introduces a general methodology for the design and control optimization of building energy systems in the life cycle. When the design scheme of building energy systems is optimized, primary steps and related issues are introduced. To improve the operation performance, the optimal control strategies that can be used by different systems are presented and key issues are discussed. To demonstrate the effect of the methods, the energy system of a high-rise building is introduced. The design on the chilled water pump system and cooling towers is improved. The control strategies for chillers, pumps and fresh air systems are optimized. The energy saving and cost from the design and control optimization methods are analyzed. The presented methodology will provide users and stakeholders an effective approach to improve the energy efficiency of building energy systems and promote the development of smart buildings and smart cities.

Smart cities have rapidly developed in the context of the integration of new digital technologies such as big data, Internet of Things, artificial intelligence, blockchains, and virtual reality. These cities have conducted practical innovations and typical cases in many sectors, such as in government, transportation, environmental protection, energy, medical care, and logistics, and have produced many social, economic, and ecological benefits. However, there are still some problems that continue to hinder the construction of smart cities. This paper examines such problems in depth and proposes some relevant countermeasures and suggestions.

In the wide context of facility management, several processes, such as operations, maintenance, retrofitting, and renovations, ensure that buildings comply with the principles of efficiency, cost-effectiveness, and indoor comfort. Apart from ordinary operation, facility management is responsible for the renovation of and long-term performance improvement of building facilities. In such a scenario, the cyber–physical system (CPS) paradigm with holonic architecture, which is the focus of this study, can successfully guide the operation management and long-term refurbishment processes of buildings. Analogous to the manufacturing field, the developed CPS maximizes holons’ self-configuration and self-organization and overall throughput effectiveness metrics to detect the best corrective actions toward system improvements. Consequently, suggestions and lessons learned from the evaluation of building efficiency are redirected to the building information model. Hence, the digital model acts as a repository of currently available equipment for operations management and the history of diagnoses that support decision-making during the maintenance, retrofitting, and renovation processes. Evidently, the repeated detection of a specific issue, which is unaffected by operations management, should be considered an opportunity to act and enhance the performances of existing building components. Similar to a goods-producing industry, the building management system developed in this study applies the aforementioned methodology to provide services related to indoor comfort and building health. This approach indicates that a method for automatic real-time diagnosis is tested in a case study consisting of a multi-use and large public building. The current paper, which is an extended version of the one presented in the Creative Construction Conference 2018, deepens the decision support tool and the supervision policy. Moreover, the developed system is contextualized by providing an example of use case and highlighting the step forward in the field of smart buildings.

The quality of information flow management has a remarkable effect on the entire life cycle of buildings. Manual retrieval of technical specifications and features of building components and their performance assessment leads to increased cost and time and efficiency reduction, especially during the facility management (FM) stage. The introduction of building information modeling (BIM) in the construction industry can provide a valuable means of improving the organization and exchange of information. BIM tools integrate multiple levels of information within a single digital model of a building. Nevertheless, the support given by BIM to FM is far from being fully effective. Technicians can benefit from real-time communication with the data repository whenever the need for gathering contextual information and/or updating any data in the digital model arises. The framework proposed in this study aims to develop a system that supports on-site operations. Information requirements have been determined from the analyses of procedures that are usually implemented in the building life cycle. These studies set the standard for the development of a digital model of a building, which will be shared among various actors in charge of FM and accessed via a cloud platform. Moreover, mixed reality is proposed to support specific information that is relevant to geometric features and procedures to be followed by operators. This article presents three use-cases supported by the proposed framework. In addition, this research article describes the first proof of concept regarding real-time support for FM.

Energy sustainability is a complex problem that needs to be tackled holistically by equally addressing other aspects such as socio-economic to meet the strict CO₂ emission targets. This paper builds upon our previous work on the effect of household transition on residential energy consumption where we developed a 3D urban energy prediction system (EvoEnergy) using the old UK panel data survey, namely, the British household panel data survey (BHPS). In particular, the aim of the present study is to examine the validity and reliability of EvoEnergy under the new UK household longitudinal study (UKHLS) launched in 2009. To achieve this aim, the household transition and energy prediction modules of EvoEnergy have been tested under both data sets using various statistical techniques such as Chow test. The analysis of the results advised that EvoEnergy remains a reliable prediction system and had a good prediction accuracy (MAPE  5%) when compared to actual energy performance certificate data. From this premise, we recommend researchers, who are working on data-driven energy consumption forecasting, to consider merging the BHPS and UKHLS data sets. This will, in turn, enable them to capture the bigger picture of different energy phenomena such as fuel poverty; consequently, anticipate problems with policy prior to their occurrence. Finally, the paper concludes by discussing two scenarios of EvoEnergy development in relation to energy policy and decision-making.