Search

• Shanghai's urban building energy model was established using diverse open data. • The reliability of open data was enhanced through multi-source data fusion. • 95 % building classification accuracy was achieved with machine learning. • Urban building energy simulation for 539,119 buildings was conducted. Urban building energy modeling is crucial for guiding carbon reduction policies, but acquiring reliable data at the urban scale remains challenging. This study develops a model for Shanghai City, China, by integrating multi-source open data. Eight data sources were collected, including maps, satellite imagery, and GIS data, covering 609,763 building footprints and 539,119 buildings (1.57 billion m2). Spatial analysis, supervised learning, and unsupervised machine learning methods were used to categorize buildings into 63 prototypes, and classification accuracy reached 95 %. Historical satellite data and community boundaries determined the year built for over 95 % of buildings. Prototypes were modeled in AutoBPS using local energy saving standards and simulated in EnergyPlus to derive energy use intensities aligning with government ranges. This work demonstrates a practical data fusion approach to develop large-scale, reliable urban building energy models. Integrating heterogeneous open data sources expands the coverage of open data and improves accuracy. The framework and insights provide a valuable foundation to leverage open data for advancing city-scale energy modeling and sustainability planning. [ABSTRACT FROM AUTHOR]

In this paper, we propose a hybrid prediction model based on spatial-temporal data fusion to predict future tunnel traffic. Our approach consists of a local predictor, a global predictor, an outlier predictor, and a prediction integrator. Firstly, the local predictor forecast tunnel traffic based on the collected local data. It is more concerned with the historical and future traffic conditions, that is, the temporal correlation. Then, the global predictor uses data collected from peripheral road segments to predict tunnel volume, which models the spatial correlation based on a deep learning network. Thirdly, the prediction integrator dynamically integrates the prediction results of local and global predictors on the basis of the current weather conditions. In addition, we detect the abnormal traffic volume for training an individual outlier predictor. Finally, we integrate it with the output of the prediction integrator and accumulate the current tunnel traffic volume to calculate final prediction results. In our experiments, we collected the multisource urban-awareness data from Shanghai to evaluate the proposed hybrid prediction model. Our approach is obviously superior to the baseline when dealing with the general condition. The MREs of 30 min and 60 min tunnel traffic volume prediction are less than 6.5%. In addition, the outlier predictor of the proposed model significantly enhances the ability to predict the abnormal tunnel traffic under extreme weather conditions or unexpected traffic accidents. [ABSTRACT FROM AUTHOR]

Abstract: Based on the authors’ research efforts over the years, an in situ structural health monitoring (SHM) technique taking advantage of guided elastic waves has been developed and deployed via an online diagnosis system. The technique and the system were recently implemented on China’s latest high-speed train (CRH380CL) operated on Beijing−Shanghai High-Speed Railway. The system incorporated modularized components including active sensor network, active wave generation, multi-channel data acquisition, signal processing, data fusion, and results presentation. The sensor network, inspired by a new concept—“decentralized standard sensing”, was integrated into the bogie frames during the final assembly of CRH380CL, to generate and acquire bogie-guided ultrasonic waves, from which a wide array of signal features were extracted. Fusion of signal features through a diagnostic imaging algorithm led to a graphic illustration of the overall health state of the bogie in a real-time and intuitive manner. The in situ experimentation covered a variety of high-speed train operation events including startup, acceleration/deceleration, full-speed operation (300km/h), emergency braking, track change, as well as full stop. Mock-up damage affixed to the bogie was identified quantitatively and visualized in images. This in situ testing has demonstrated the feasibility, effectiveness, sensitivity, and reliability of the developed SHM technique and the system towards real-world applications. [Copyright &y& Elsevier]