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Application of Building Information Modeling in Energy Management of Individual Departments Occupying University Facilities

Authors: Kung-Jen Tu, Danny Vernatha

Abstract:

To assist individual departments within universities in their energy management tasks, this study explores the application of Building Information Modeling in establishing the ‘BIM based Energy Management Support System’ (BIM-EMSS). The BIM-EMSS consists of six components: (1) sensors installed for each occupant and each equipment, (2) electricity sub-meters (constantly logging lighting, HVAC, and socket electricity consumptions of each room), (3) BIM models of all rooms within individual departments’ facilities, (4) data warehouse (for storing occupancy status and logged electricity consumption data), (5) building energy management system that provides energy managers with various energy management functions, and (6) energy simulation tool (such as eQuest) that generates real time 'standard energy consumptions' data against which 'actual energy consumptions' data are compared and energy efficiency evaluated. Through the building energy management system, the energy manager is able to (a) have 3D visualization (BIM model) of each room, in which the occupancy and equipment status detected by the sensors and the electricity consumptions data logged are displayed constantly; (b) perform real time energy consumption analysis to compare the actual and standard energy consumption profiles of a space; (c) obtain energy consumption anomaly detection warnings on certain rooms so that energy management corrective actions can be further taken (data mining technique is employed to analyze the relation between space occupancy pattern with current space equipment setting to indicate an anomaly, such as when appliances turn on without occupancy); and (d) perform historical energy consumption analysis to review monthly and annually energy consumption profiles and compare them against historical energy profiles. The BIM-EMSS was further implemented in a research lab in the Department of Architecture of NTUST in Taiwan and implementation results presented to illustrate how it can be used to assist individual departments within universities in their energy management tasks.

Keywords: Sensor, electricity sub-meters, database, energy anomaly detection.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1112113

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References:


[1] Tu, K.J. & Lin, C.H. (2012). Benchmarking Energy Efficiency by 'Space Type': An Energy Management Tool for Individual Departments within Universities. Journal of Asian Architecture and Building Engineering JJABE, 299-306.
[2] Pennsylvania State University. (2012). BIM Guide: Penn State - BIM Planning Guide for Facility Owners. Retrieved May 26, 2015.
[3] Volk, R., Stengel, J., & Schultmann, F. (2014). Building Information Modeling (BIM) for existing buildings - Literature review and future needs. Automation in Construction, 109-127.
[4] Becerik-Gerber, B., Jazizadeh, F., Li, N., & Calis, G. (n.d.). Application Areas and Data Requirements for BIM-Enabled Facilities Management. Journal of Construction Engineering and Management J. Constr. Eng. Manage., 431-442.
[5] U.S. General Services Administration. (2012). GSA BIM Guide Series 05 - BIM Guide for Energy Performance v2. Retrieved May 14, 2015.
[6] Acquaviva, A., Blaso L., & Dalmaso, D. (n.d.) Energy consumption management using CAFM and BIM applications. X Forum Internazionale di Studi.
[7] Chen, J., Bulbul, T., Taylor, J., & Olgun, G. (2014). A Case Study of Embedding Real-time Infrastructure Sensor Data to BIM. Construction Research Congress 2014.
[8] Ruzzelli, A., Nicolas, C., Schoofs, A., & O'hare, G. (n.d.). Real-Time Recognition and Profiling of Appliances through a Single Electricity Sensor. 2010 7th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON).
[9] Wu, S., & Clements-Croome, D. (2007). Understanding the indoor environment through mining sensory data - A case study. Energy and Buildings, 1183-1191.
[10] Alahmad, M., Nader, W., Brumbaugh, A., Cho, Y., Ci, S., Sharif, H., ... Neal, J. (2011). The BIM's 4D dimension: Real time energy monitoring. 2011 IEEE GCC Conference and Exhibition (GCC).
[11] Ramtin, A., Hailemariam, E., Glueck, M., Tessier, A., McCrae, J., & Khan, A. (2010). BIM-based Building Performance Monitor - Publications Retrieved May 25, 2015, from http://autodeskresearch.com/ publications/ bimdashboardvideo
[12] Dae Kyo, J., Donghwan, L., & Seunghee, P. (2014). Energy Operation Management for Smart City using 3D Building Energy Information Modeling. International Journal Of Precision Engineering And Manufacturing, 1717-1724.
[13] Osello, A., Acquaviva, A., Agherno, C., & Blaso, L. (2013). Energy saving in existing buildings by an intelligent use of interoperable ICTs. Energy Efficiency, 707–723.
[14] Gökçe, H., & Gökçe, K. (n.d.). Holistic System Architecture for Energy Efficient Building Operation. Sustainable Cities and Society, 77-84.
[15] Ufuk Gökce, H., Ufuk Gökce, K. (2013). Virtual Energy Platform for Low Energy Building Operations. Progress in Sustainable Energy Technologies, 11, 319-331.
[16] Ahmed, A., Korres, N., Ploennigs, J., Elhadi, H., & Menzel, K. (2011). Mining building performance data for energy-efficient operation. Advanced Engineering Informatics, 341-354.
[17] Ann Piete, M. Kartar Kinney, S. Haves, P. (2001). Analysis of an Information Monitoring and Diagnostic System to Improve Building Operations. Energy and Buildings 33, 783-791.
[18] Costa, A., Keane, M., Torrens, J., & Corry, E. (2013). Building operation and energy performance: Monitoring, analysis and optimization toolkit. Applied Energy, 310-316.
[19] Hong, T., Yang, L., Hill, D., & Feng, W. (2014). Data and analytics to inform energy retrofit of high performance buildings. Applied Energy, 126, 90-106.
[20] Pérez-Lombard, L., Ortiz, J., & Pout, C. (2007). A Review On Buildings Energy Consumption Information. Energy and Buildings, 394-398.
[21] Dong, B., O'neill, Z., & Li, Z. (2014). A BIM-enabled information infrastructure for building energy Fault Detection and Diagnostics. Automation in Construction, 197-211.
[22] Kumar, S., Sinha, S., Kojima, T., & Yoshida, H. (2001). Development of Parameter Based Fault Detection and Diagnosis Technique for Energy Efficient Building Management System. Energy Conversion and Management, 833-854.
[23] Shih, H. (2014). A Robust Occupancy Detection and Tracking Algorithm for The Automatic Monitoring and Commissioning of a Building. Energy and Buildings, 270-280.