Intelligent building management systems are proven to lead to energy savings and are an integral component of smart buildings. The procedures developed in the EN standards describe the methodology for calculating the energy savings achieved by improving the automation and control levels of separate services in building systems. However, although this method is used in practice, it is rarely applied or investigated by the research community. Typically, energy savings resulting from a single automation improvement intervention in a building heating system are observed, while the holistic view of combined automation upgrades is not considered. Therefore, the purpose of this study was to assess the energy savings resulting from several upgrades to control levels in the heating system components of the building. In addition, this research provides a rationale for the impact of multiple automation and control options for heating systems as well as examines the difference in energy savings. Finally, an analytical model is developed and demonstrated to assess the feasibility of building automation and control upgrades, by determining the allowed investment according to a set of predefined indicators.
While the AEC industry is moving towards digitalization off-site rebar prefabrication became a common practice. Now most companies use a long-established standard order processing method, where the customer submits 2D paper or PDF-based drawings. Subsequently, the manufacturers are obligated to make additional detailing, redrawing, calculations, and preparation of other required information for manufacturing. Thus, in this typical scenario, there is a great repetition of the same tasks, with the obvious loss of time and increased likelihood of human error. However, improvements can be made by the application of advanced digital production workflow and the use of open BIM standards (e.g., IFC, XML, BVBS). Therefore, this paper presents the typical data flow algorithm in contrast to the automated data flow for reinforcement manufacturing. Further, the two approaches are compared and analyzed based on Multi-Criteria Decision Making (MCDM) methods. The results have shown promising prospects for companies willing to automate their data flow processes by the use of 3D drawings and digital data from the BIM model in their plants.
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