Recently, fire accidents in buildings have become bigger around the world, and it has become necessary to build an efficient building disaster management system suitable for fires in a Smart City. As building fires increase the number of casualties and property damage, it is necessary to take appropriate action accordingly. There has been an increasing effort to develop such disaster management systems worldwide by applying information communication technology (ICT), and many studies have been conducted in practice. In this paper, an augmented reality (AR)-based Smart Building and Town Disaster Management System is suggested in order to acquire visibility and to grasp occupants in case of fire disasters in buildings. This system provides visualization information and optimal guide for quick initial response by utilizing smart element AR-based disaster management service through linkage of physical virtual domain in the building. Additionally, we show a scenario flow chart of the fire extinguishment process according to the time from the ignition stage to the extinguishment stage in the building. Finally, we introduce the related sensors, the actuators, and a small test-bed for AR-based disaster management service. This test-bed was designed for interlocking and interoperability test of the system between the sensors and the actuators. It is expected that the proposed system can provide a quick and safe rescue guideline to the occupants and rescuers in the building where fire is generated and in regions of poor visibility.

Currently, many intelligent building energy management systems (BEMSs) are emerging for saving energy in new and existing buildings and realizing a sustainable society worldwide. However, installing an intelligent BEMS in existing buildings does not realize an innovative and advanced society because it only involves simple equipment replacement (i.e., replacement of old equipment or LED (Light Emitting Diode) lamps) and energy savings based on a stand-alone system. Therefore, artificial intelligence (AI) is applied to a BEMS to implement intelligent energy optimization based on the latest ICT (Information and Communications Technologies) technology. AI can analyze energy usage data, predict future energy requirements, and establish an appropriate energy saving policy. In this paper, we present a dynamic heating, ventilation, and air conditioning (HVAC) scheduling method that collects, analyzes, and infers energy usage data to intelligently save energy in buildings based on reinforcement learning (RL). In this regard, a hotel is used as the testbed in this study. The proposed method collects, analyzes, and infers IoT data from a building to provide an energy saving policy to realize a futuristic HVAC (heating system) system based on RL. Through this process, a purpose-oriented energy saving methodology to achieve energy saving goals is proposed.

The scale of human accidents and the resultant damage has increased due to recent large-scale urban (building) fires, meaning there is a need to devise an effective strategy for urban disasters. In the event of a fire, it is difficult to evacuate in the early stages due to the loss of detection function, difficulty in securing visibility, and confusion over evacuation routes. Accordingly, for rapid evacuation and rescue, it is necessary to build a city-level fire safety service and digital system based on smart technology. In addition, both forest and building fires emit a large amount of carbon dioxide, which is the main cause of global warming. Therefore, we need to prepare both energy and fire management to achieve carbon neutrality by 2030. In this study, we developed an AI-based smart fire safety system for efficient urban integrated management using a city-based fire safety architecture. In addition, we designed a fire management infrastructure and an energy management system for buildings. The proposal was demonstrated by building a test bed in the A building, and the AR-based mobile/web application was tested for optimized evacuation management. Furthermore, AI-based fire detection and the optimal evacuation of occupants were implemented through deep learning-based fire information data analysis. As a result, this paper presents four points for safety and energy management, and we demonstrate that the optimization of occupant evacuation ability and energy saving can be achieved. We also analyze the efficiency of the data transfer rate to prevent data communication delays by using Virtual Edge Gateway (VEG) management. In the future, we expect that the appearance of future fire and energy management buildings through this research will produce more accurate data prediction technology and the development of cutting-edge smart technology in smart city infrastructures.

The electric vehicle (EV) industry has emerged in response to the necessity of reducing greenhouse gas emissions and combating climate change. However, as the number of EVs increases, EV charging networks are confronted with considerable obstacles pertaining to accessibility, charging time, and the equilibrium between electricity demand and supply. In this paper, we present a techno-economic analysis of EV charging stations (EVCSs) by building type. This analysis is based on public EVCS data and considers both standalone local grid operation and integrated operation of distributed energy resources (DERs) and the local grid. The analysis has significant implications for the management of the electricity grid and the utilization of sustainable energy, and can result in economic benefits for both residential, commercial, and public buildings. The analysis indicates that integrating DERs with the local grid at EV charging stations can reduce local grid usage relative to EV demand. Nevertheless, there are also complexities, such as initial investment and maintenance costs, especially the weather-dependent performance variability of solar, which require financial support mechanisms, such as subsidies or tax incentives. Future research should focus on different DER integrations, regional and seasonal variability, user behavior, installation location, policy and regulatory impacts, and detailed capital expenditure analysis. Such research will advance DER and EVCS integration and contribute to increasing the efficiency and sustainability of urban energy systems.

Energy prediction models and platforms are being developed to achieve carbon-neutral ESG, transition buildings to renewable energy, and supply sustainable energy to EV charging infrastructure. Despite numerous studies on machine learning (ML)-based prediction models for photovoltaic (PV) energy, integrating models with carbon emission analysis and an electric vehicle (EV) charging platform remains challenging. To overcome this, we propose a building-specific long short-term memory (LSTM) prediction model for PV energy supply. This model simulates the integration of EV charging platforms and offer solutions for carbon reduction. Integrating a PV energy prediction model within buildings and EV charging platforms using ICT is crucial to achieve renewable energy transition and carbon neutrality. The ML model uses data from various perspectives to derive operational strategies for energy supply to the grid. Additionally, simulations explore the integration of PV-EV charging infrastructure, EV charging control based on energy, and mechanisms for sharing energy, promoting eco-friendly charging. By comparing carbon emissions from fossil-fuel-based sources with PV energy sources, we analyze the reduction in carbon emission effects, providing a comprehensive understanding of carbon reduction and energy transition through energy prediction. In the future, we aim to secure economic viability in the building energy infrastructure market and establish a carbon-neutral city by providing a stable energy supply to buildings and EV charging infrastructure. Through ongoing research on specialized models tailored to the unique characteristics of energy domains within buildings, we aim to contribute to the resolution of inter-regional energy supply challenges and the achievement of carbon reduction.