ROOM-BASED ENERGY DEMAND CLASSIFICATION OF BIM DATA USING GRAPH SUPERVISED LEARNING

Nowadays, cities and buildings are increasingly interconnected with new modern data models like the 3D city model and Building Information Modelling (BIM) for urban management. In the past decades, BIM appears to have been primarily used for visualization. However, BIM has been recently used for a wide range of applications, especially in Building Energy Consumption Estimation (BECE). Despite extensive research, BIM is less used in BECE data-driven approaches due to its complexity in the data model and incompatibility with machine learning algorithms. Therefore, this paper highlights the potential opportunity to apply graph-based learning algorithms (e.g., GraphSAGE) using the enriched semantic, geometry, and room topology information extracted from BIM data. The preliminary results are demonstrated a promising avenue for BECE analysis in both pre-construction step (design) and post-construction step like retrofitting processes.

Teleconsultation demand classification and service analysis

Background To enhance teleconsultation management, demands can be classified into different patterns, and the service of each pattern demand can be improved. Methods For the effective teleconsultation classification, a novel ensemble hierarchical clustering method is proposed in this study. In the proposed method, individual clustering results are first obtained by different hierarchical clustering methods, and then ensembled by one-hot encoding, the calculation and division of cosine similarity, and network graph representation. In the built network graph about the high cosine similarity, the connected demand series can be categorized into one pattern. For verification, 43 teleconsultation demand series are used as sample data, and the efficiency and quality of teleconsultation services are respectively analyzed before and after the demand classification. Results The teleconsultation demands are classified into three categories, erratic, lumpy, and slow. Under the fixed strategies, the service analysis after demand classification reveals the deficiencies of teleconsultation services, but analysis before demand classification can’t. Conclusion The proposed ensemble hierarchical clustering method can effectively category teleconsultation demands, and the effective demand categorization can enhance teleconsultation management.

A classification matching method for manufacturing resource in cloud manufacturing environment

Cloud manufacturing (CMfg), combining the idea and technologies of cloud computing and Internet of Things, is an emerging service-oriented manufacturing model. The supply–demand matching of manufacturing resources is one of the key technologies for implemention. However, resources in CMfg system are geographically distributed, functional of similar and dynamically changeable, and these features make it difficult to obtain higher accuracy for existing matching methods. In order to select the most satisfied resources in CMfg, a semantics-based supply–demand classification matching method (SDCM) is proposed. Firstly, the implementing framework of SDCM is constructed. Then, combined with the theories of ontology and dynamic description logic, a semantics-based SDCM algorithm is designed, which includes four implementation stages, respectively, basic information matching, IOPE parameters (Input, Outputs, Preconditions, Effects) matching, QoS (Quality of Service) matching and comprehensive matching. Finally, a case verifies the feasibility and effectiveness of the proposed method.