With the development and popularization of the Internet of Things, big data, cloud computing, and other technologies, Digital twin technology (DTT) is increasingly applied to building operation and maintenance (O&M). However, most of the research focuses on building energy consumption, safety, and other management, and relatively little research on the monitoring of building terminal energy using information. The information is closely related to occupants’ behavior, such as air conditioning, lighting, shading, window status information, as well as personnel number and location, and it has a great impact on building energy consumption. Due to different occupants’ behaviors, the level of building energy consumption varies several times or even more. Take an office building as an example. Based on digital twin technology, the framework of building energy using intelligent monitoring is constructed. It mainly includes four parts, namely building physical space, virtual twin space, predictive control simulation engine, and twin big data. For each part, functions are realized through building Information Modelling (BIM), smart sensors, and Internet of Things (IoT) technologies. Based on the standard framework and every function realization method, the DTT can used for building O&M effectually. The application of building an intelligent control system based on the occupants’ characteristics is simulated and analyzed in Designbuilder software 6.1.2. The results show that the digital technology application in building intelligent control systems can realize maximum energy saving for 30%. However, the DTT in building O&M is not widely used now. There is a lot of research to be completed in the future.

The radiant cooling system has a substantial energy-saving effect and can be widely applied in different kinds of low-energy buildings. This article reports the experimental study of the design strategy of the radiant cooling system in low-energy buildings from the perspective of thermal response characteristics of an indoor environment. Two types of a modular radiant cooling system, namely, the copper tube radiant cooling (CTRC) and the capillary radiant cooling (CRC) systems, were investigated. The experiments were conducted in two office rooms characterized by low energy consumption. In total, 16 cases (eight for CTRC and eight for CRC) were analyzed, covering supply water temperature with a range of 12–19 °C. The experimental results show that the supply water temperature has a more substantial effect on the temperature distribution of the envelope for CTRC, than that of CRC. The indoor air temperature stratification is acceptable in the active area of the occupant with a modular radiant cooling system. Moreover, the thermal response of the envelope is highly sensitive to the lower supply water temperature (below 16 °C) using CTRC and to the higher supply water temperature (above 15 °C) using CRC. The low supply water temperature (below 15 °C) can improve the thermal stability speed of indoor air to a greater degree using CTRC, than that of CRC. The supply water temperature for CTRC with 15–16 °C, and 18–19 °C for CRC in low-energy buildings can exert an optimal cooling benefit.

With governments worldwide emphasizing environmental protection and the global focus on carbon reduction, the battery electric vehicle (BEV) industry has developed rapidly. An urban transportation network with BEVs as the main form of transportation will soon become mainstream. Motivated by the abovementioned background, a BEV transportation network design problem is investigated, and a network design model is established. The model aims to minimize the system travel time of BEV transportation networks and optimize the government’s lane expansion scheme (the location and number of lanes) under a limited budget. To consider the travel characteristics of BEV drivers, the charging time, range anxiety, and bounded rationality factors are simultaneously incorporated into the model. A heuristic algorithm is designed based on the active set algorithm to obtain the local optimal solution to the actual-scale problem. Moreover, a cutting-plane method is used to convert the original problem into a different form, and a column generation technique is embedded in the abovementioned algorithm to avoid the enumeration of paths. Sensitivity analyses of different levels of rationality of BEV drivers and government investment scales are performed. The experimental results demonstrate that the model and algorithm can effectively solve the problem and provide decision support for the government in formulating transportation infrastructure construction policies.

Shading facilities are important technology to enable the adjustment of the indoor light and heat environment, and the control logic of the technology relies on data collected by sensors. The sensor position is generally arranged on the work surface, which is only suitable for single rooms with fixed locations. For open-plan offices or other large offices, more study of detailed designs for the sensor position is required. Therefore, various sensor locations for different spaces will be investigated. Based on existing research, the UDI_{2000 lux} [50%] and UDI_{450–2000 lux} [50%] are the key indices for measuring sensor location. The Entropy Weight method is used to determine the weight of each index, and the ideal point method (TOPSIS method) is used to select the best sensor location. Based on the results, recommendations are provided for different space scales, window-to-wall ratios, and building orientations of offices for shading control sensor location.