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Asset Management (AM) for buildings is a systematic process for organising, planning, operating, maintaining, and upgrading physical assets of the building efficiently. AM enhances the overall building service delivery potential and minimizes their costs and risks over its whole life (McElroy 1999; Frolov et al. 2010). Generally, AM has often been a practice of response activities to maintain and operate following the simplistic guideline of cost saving. This approach has led to the upsurge of the operation cost by 20% and the necessary energy consumption by 30% (Pinheiro et al. 2018). Nowadays, many organizations and researchers have introduced different innovations and technologies such as Building Information Modelling (BIM), Linked Data and Big Data Analytics to attain effective asset management (Farghaly et al. 2017). Vanier (2001) identified two main challenges in achieving effective asset management; namely, seamless data integration and lack of a standardization framework and process map. Woodhouse (1997) stated that the main challenge in AM, rather than the technical barriers of implementing AM and related innovations, lies on the human error in data collection and analysis which leads to missing and inadequate data.
Consequently, AM requires an information system that automatically captures, stores, and integrates data required to support better decision-making. It has been argued that BIM can provide the required information system for solving asset management challenges (Becerik-Gerber et al. 2012; Kelly et al. 2013; Teicholz 2013; Ibrahim et al. 2016). BIM is an innovative benchmark driven by technology where information of a facility can be digitally 3D represented and shared forming a reliable basis for decisions during its lifecycle from inception onward. Thus, in the last decade, the implementation of BIM in AM has significantly increased among practitioners (Dias and Ergan 2020). This has been due to the noticeable BIM capability in collecting, capturing and generating data/information during the lifecycle of assets through open data standards such as the Industry Foundation Classes (IFC) and MVDs such as the Construction Operations Building information exchange (COBie) standard (Tang et al. 2020). However, accuracies of data exchanged between the BIM and AM systems is still the main challenges for owners and asset managers (Cavka et al. 2017; Farghaly 2019). To manage, maintain and operate a complete building system, more information is required to be provided more than COBie and IFC ones (NIBS 2015). Consequently, U.S. Army, Corps of Engineers, Engineer Research and Development Center initiated several projects for each system model. MVD has been developed for each system such as HVACie for Heating, ventilation, and air conditioning system, SPARKie for electrical system and WSie (Water System Information Exchange) for water system (NIBS 2015). These MVDs provide the geometry of the system and a subset of each MVDs can be represented in COBie. However, due to the heterogeneity of the assets and buildings, it has been argued that the required information cannot be generalized for all assets or even by the asset system (Cavka et al. 2017). Farghaly et al. (2018) suggested that the required information taxonomy can be generalized and developed for assets based on their functionality in specific building type. Based on the aforementioned considerations and suggestions, this study represents the development of a new Asset Consuming Energy Information Exchange (ACEie) MVD for data exchange of assets that consume energy from the BIM systems to the AM systems during the handover stage.