The COVID-19 pandemic has spread rapidly all over the world, affecting many countries to varying degrees. In this study, an in-depth analysis of the factors influencing the spread of COVID-19 is offered mainly through big data in the European Union (EU) context. In doing so, the data of the first wave of the pandemic are assessed. Afterward, we evaluate the impacts of the COVID-19 spread in specific countries and regions. Based on the existing literature, mobility is recognized as a significant direct factor affecting disease transmission. The same applies to the case of COVID-19. However, compared with the analysis of mobility itself, this paper explores more profound reasons that affect mobility, ranging from policy and economy to geographical and transportation factors. Specifically, this paper studies nine EU countries based on their population density and the degree of impact of the epidemic in the first six months (February to July 2020) of the pandemic. Our study aims to illustrate how policies, economies, and geographical locations (including transportation factors) directly or indirectly affect the spread of the novel coronavirus by applying the SEIR model to analyze all selected countries’ big data. The key findings of this research are: (1) the timeliness of relevant policies and the effectiveness of government implementation indirectly limit the spread of the epidemic by reducing population mobility; (2) a better medical level would contribute to detect, isolate, and treat patients, and help control the epidemic; and (3) the large land borders and developed transportation between countries exacerbate the spread of the COVID-19. The paper contributes to ongoing research on COVID-19 by addressing the above points.

Building-energy consumption is the primary aim of urban energy consumption, which can aid in optimization of building operation and management techniques, creating sustainable building and built environments. However, modellers’ understanding of the relationship between building-energy modelling (BEM) accuracy and computational load is still qualitative and deprived of accurate quantitative study. Based on a bottom-up engineering methodology, this study aims to quantitatively explore the effects of building-model input data with different resolution accuracies on energy simulation results, including evaluation of computational load. According to the actual parameters of the case-study building, 108 models with varying input resolution levels were developed to estimate hourly energy usage and annual mean ambient temperature. The results demonstrated that with input parameters at low resolution levels, geometric parameters such as exterior windows, interior windows, and shading exhibited significantly lower computational loads, resulting in reduced errors in the final simulation performance, whereas the occupancy schedule, thermal zoning, and HVAC configuration parameters exhibited significant declines in simulation performance and accuracy. This study presents a methodology applicable to the majority of low-rise, rectangular office structures. Future work would concentrate on carrying out comparison tests for different building forms and types while gradually improving the automation of the process to enable use of the appropriate accuracy level in assessing the crucial issue of energy-modelling input.

The transition of the energy model dominated by centralized fossil energy use and the emergence of the Energy Internet and the Integrated Community Energy System (ICES) has gained attention. ICES involved the connection of electricity, heat, gas, and other kinds of energy, and was a significant form of the targeted transformation of conventional single energy networks. Within this system, the traditional demand response (DR) was transformed into an integrated demand response (IDR) in which all energy consumers could participate. The purpose of this study is to discuss the important technologies and models along with assessment and optimization strategies for the implementation of ICES and IDR, based on an extensive literature review. The analysis results show the “IDR + ICES” ecosystem proved to hold great potential for achieving renewable energy penetration, energy efficiency, and climate change control goals, while there are still many limitations in the coordination and reliability of the model and the design of the market mechanism. To conclude, the challenges and opportunities that ICES and IDR face were summarized, and future avenues for research are outlined.

Building energy flexibility is essential for integrating renewables, optimizing energy use, and ensuring grid stability. While renewable and storage systems are increasingly used in buildings, poorly designed storage strategies often cause supply-demand mismatches, and a comprehensive, indicator-based assessment approach for quantifying flexibility remains lacking. Therefore, this study designs customized energy storage regulation strategies and constructs a comprehensive energy flexibility assessment scheme to address key issues in supply-demand coordination and energy flexibility evaluation. LSTM and Rolling-XGB methods are used to predict building energy consumption and PV generation, respectively. Based on battery safety constraints, a data-driven battery energy storage system (BESS) model simulates battery behavior to evaluate and compare building energy flexibility under two scenarios: (1) uncoordinated PV-BESS, and (2) coordinated PV-BESS with load forecasting. A practical validation was conducted using a net-zero-carbon building as the case study. Simulation results show that the data-driven BESS model improves building energy flexibility and reduces electricity costs through optimized battery sizing, tailored storage strategies, and consideration of local time-of-use tariffs. In the case study, local energy coverage reached 62.75%, surplus time increased to 34.77%, and costs were cut by nearly 40% compared to the PV-only scenario, demonstrating the significant benefits brought by the proposed BESS model that integrates load forecasting and PV generation prediction features.

With the popularization of Virtual Reality (VR) technology, the application of the complementary advantages of BIM+VR collaboration in facility management (FM) and asset management (AM) is an emerging research field. This theory holds that BIM information generated and acquired in the life cycle of the facility can improve the management of the facility and asset based on the visual expression of VR technology. With this proposition as a starting point, based on the Ningbo Lishe International Airport’s staff restaurant project, the purpose of this paper is to investigate the value of BIM and VR and the challenges they pose in influencing their adoption in AM applications. By establishing a BIM model and a quantitative asset information model, and using the 720 cloud VR platform and related smart devices to complete the visual expression of asset information. The results demonstrated that that the value of BIM+VR in AM comes from the improvement of the existing manual information transfer process, which reduces the data delay to a large extent. Secondly, the accuracy and accessibility of AM data are improved, and the efficiency of the management is improved. The originality of this paper is to prove the great potential of BIM and VR in AM optimization and improvement through real case study, and to provide empirical evidence for the value and challenges of BIM+VR in AM application.