Abstract: Buildings in Europe account for 40 percent of all energy use; therefore, it is crucial to monitor building energy usage levels, conditions originating from the building, and external factors like user behaviors and climate to generate accurate forecasts. Digital twins, gbXML models, and energy models are a few techniques to forecast potential energy usage,but the gap between real-life data results has a significant gap, therefore gathering and evaluating actual data is crucial for energy consumption prediction. Building Management Systems (BMS) and other smart building technologies, such sensor and Internet of Things (IoT) devices, are becoming widely used in new buildings and generate and gather a large quantity of data. Building information(BIM) modeling can be used to utilize this data, implement it into buildings, and produce meaningful implementation. Compiling these multiple data kinds from various sources and developing a common understanding is also compatible with BIM, which is a representation of the building, in order to comprehend the ways to combining these diverse data types is the purpose of the paper. To gain a basic understanding of the relationship between time series data and IFC models, temperature sensor values were projected into the IFC Model of the Umar Unit of the Nest Building.
1. Introduction
The forecast of energy usage in buildings is crucial in order to optimize their energy efficiency, with the goal of attaining energy conservation and improving environmental effect considering buildings account for 40 percent of all energy use in Europe [1]. To achieve better forecasting, monitoring data, and connecting the consumption with internal-building related, and external-user, climate related factors should be processed. Collecting data through sensors and IoT devices, maintenance of this data, forming useful and understandable output can be helpful to create more accurate predictions. Building information modeling (BIM) addresses the continuous transfer of digital information throughout the entire life-cycle of a building complex, from the design and construction phases to operation [2]. BIM models are also machine readable information format contains geometry and further about the building, therefore convenient context to constitute a linking system, or a pattern for diverse data. Linking diverse data through BIM Models, presents data in a easily graspable way, and defining efficient usage of new technologies in the building environment makes interoperability and simplification of data very valuable [3]. Therefore first, creating a connection of sensor values and BIM model after visualizing the data over a building to broaden the target audience of the possible applications of the BMS was studied with the data from Umar Unit of NEST building.
1.1. Nest Building: Next Evolution in Sustainable Building Technologies(NEST) (Figure 2) is a research and development center located in Dubendorf, Switzerland, of two Swiss Research institutes, Swiss Federal Laboratories for Materials Science and Technology(EMPA) and Swiss Federal Institute of Aquatic Science and Technology (Eawag) [4]. Nest has a constant core and three free platforms allow, different units to be implemented in the building, creating the possibility to replace units as their purpose is completed, and opening up space for further research [4]. The research center creates an environment with advanced and innovative building technologies to test, better, and exhibit the applications in real-life circumstances[5]. Quarable Sensor Time Series Data monitored in web server, Sensor Metadata, and BIM Models (Revit and IFC) alongside gbXML model and Digital Twin for UMAR unit are the references for the case study, also knowledge graph broadcasting on GraphDB servers.
1.2. Problem definition: NEST Building (Figure - 2) collects and stores various types of data such as BIM, sensor data, schematics, and user interfaces, interoperability and interconnectivity of different types is required to constitute meaningful results and usage of the innovative systems and information obtained from it. Creating the connection between sensor, GUI and Interfaces, and deriving precise information of the building in real time are the objectives of the study.
1.3. Research approach: ”Room number” served as the foundation for all element-related input, and the BIM model, Time Series Data, and energy estimations all share a common room number value. As a result, the building’s room number values are identical by the Revit Model, ”IfcSpace” elements, and Time Series Data values, which all respond to spaces as a common aspect. Due to the comprehensiveness of their geometry, IfcSpace elements acted as a bridge between, as well as an element that could project information.
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