Applications of Clustering and Isolation Forest Techniques in Real-Time Building Energy-Consumption Data: Application to LEED Certified Buildings
Publication: Journal of Energy Engineering
Volume 143, Issue 5
Abstract
Buildings are the largest consumer of energy in the United States from various sectors that includes transportation, industry, commercial, and residential buildings. Leadership in Energy and Environmental Design (LEED) certification program, home energy rating system (HERS), and American Society of Heating, Refrigerating and Air-conditioning Engineers (ASHRAE) standards are developed to improve the energy efficiency of the commercial and residential buildings. However, these programs, codes, and standards are used before or during the design and construction phases. For this reason, it is challenging to track whether buildings still could be energy efficient post construction. The primary purpose of this study was to detect the anomalies from the energy consumption dataset of LEED institutional buildings. The anomalies are identified using two different data mining techniques, which are clustering, and isolation Forest (iForest). This paper demonstrates an integrated data mining approach that helps in evaluating LEED energy and atmosphere (EA) credits after construction.
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References
Abe, N., Zadrozny, B., and Langford, J. (2006). “Outlier detection by active learning.” Proc., 12th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, ACM, New York, 504.
Akoglu, L., Tong, H., and Koutra, D. (2015). “Graph-based anomaly detection and description: A survey.” Data Min. Knowl. Discovery, 29(3), 626–688.
Alizadeh, E., Meskin, N., Benammar, M., and Khorasani, K. (2013). “Fault detection and isolation of the wind turbine based on the real-valued negative selection algorithm.” 7th IEEE GCC Conf. and Exhibition, IEEE, New York, 11–16.
Azhar, S., Brown, J., and Sattineni, A. (2010). “A case study of building performance analyses using building information modeling.” Proc., 27th Int. Symp. on Automation and Robotics in Construction (ISARC-27), Bratislava, Slovakia, 25–27.
Barse, E. L., Kvarnstrom, H., and Jonsson, E. (2003). “Synthesizing test data for fraud detection systems.” Proc., 19th Annual Computer Security Applications Conf., IEEE, New York, 384–394.
Begovich, O., and Valdovinos-Villalobos, G. (2010). “DSP application of a water-leak detection and isolation algorithm.” 7th Int. Conf. on Electrical Engineering Computing Science and Automatic Control (CCE), IEEE, New York, 93–98.
Bhuyan, M. H., Bhattacharyya, D. K., and Kalita, J. K. (2014). “Network anomaly detection: Methods, systems and tools.” IEEE Commun. Surv. Tutorials, 16(1), 303–336.
Blake, C., and Merz, C. J. (2015). “UCI repository of machine learning databases.” Dept. of Information and Computer Science, Univ. of California, Irvine, CA, ⟨http://archive.ics.uci.edu/ml⟩ (Apr. 25, 2016).
Bonchi, F., Giannotti, F., Mainetto, G., and Pedreschi, D. (1999). “A classification-based methodology for planning auditing strategies in fraud detection.” Proc., 5th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, ACM, New York, 175–184.
Bose, S., and Diette, G. B. (2016). “Health disparities related to environmental air quality.” Health disparities in respiratory medicine, Springer, Berlin, 41–58.
Carrasquilla, U. (2010). “Benchmarking algorithms for detecting anomalies in large datasets.” MeasureIT, 1–16.
Chaturvedi, S. K., Richariya, V., and Tiwari, N. (2012). “Anomaly detection in a network using data mining techniques.” Int. J. Emerging Technol. Adv. Eng., 2, 2250–2459.
Chicco, G., Napoli, R., and Piglione, F. (2006). “Comparisons among clustering techniques for electricity customer classification.” IEEE Trans. Power Syst., 21(2), 933–940.
Davidson, I., and Ravi, S. S. (2007). “The complexity of non-hierarchical clustering with instance and cluster level constraints.” Data Min. Knowl. Discovery, 14(1), 25–61.
Diamond, R., Opitz, M., Hicks, T., Vonneida, B., and Herrera, S. (2006). “Evaluating the site energy performance of the first generation of LEED-certified commercial buildings.” Proc., 2006 Summer Study on Energy Efficiency in Buildings, American Council for an Energy-Efficient Economy, Washington, DC.
Ding, G. K. (2008). “Sustainable construction—The role of environmental assessment tools.” J. Environ. Manage., 86(3), 451–464.
Fenghua, Z., Weidong, Q., and Yupu, Y. (2013). “One fast fault isolation algorithm based on Bayesian network.” Chinese Automation Congress, IEEE, New York, 17–22.
Fowler, K. M., and Rauch, E. M. (2006). “Sustainable building rating systems summary.”, Pacific Northwest National Laboratory, Richland, WA.
Gogoi, P., Bhattacharyya, D. K., Borah, B., and Kalita, J. K. (2011). “A survey of outlier detection methods in network anomaly identification.” Comput. J., 54(4), 570–588.
Grubb, M., et al. (1991). “Energy policies and the greenhouse effect: A study of national differences.” Energy Policy, 19(10), 911–917.
Han, J., and Kamber, M. (2006). “Classification and prediction.” Data mining: Concepts and techniques, Morgan Kaufmann Publisher, San Francisco, 285–382.
Helman, P., Liepins, G., and Richards, W. (1992). “Foundations of intrusion detection (computer security).” Proc., Computer Security Foundations Workshop V, IEEE, New York, 114–120.
Hodge, V. J., and Austin, J. (2004). “A survey of outlier detection methodologies.” Artif. Intell. Rev., 22(2), 85–126.
Jain, A. K., Murty, M. N., and Flynn, P. J. (1999). “Data clustering: A review.” ACM Comput. Surv., 31(3), 264–323.
Jian, K., et al. (2009). “A performance isolation algorithm for shared virtualization storage system.” IEEE Int. Conf. on Networking, Architecture, and Storage, IEEE, New York, 35–42.
Kwok, K. Y. G., Kim, J., Chong, W. K., and Ariaratnam, S. T. (2016). “Structuring a comprehensive carbon-emission framework for the whole lifecycle of building, operation, and construction.” J. Archit. Eng., 04016006.
Lappas, T., and Pelechrinis, K. (2007). Data mining techniques for (network) intrusion detection systems, Dept. of Computer Science and Engineering UC Riverside, Riverside, CA, 92521.
Liu, F. T., Ting, K. M., Yu, Y., and Zhou, Z. H. (2008). “Spectrum of variable-random trees.” J. Artif. Intell. Res., 32, 355–384.
Liu, F. T., Ting, K. M., and Zhou, Z. H. (2010). “Can isolation-based anomaly detectors handle arbitrary multi-modal patterns in data?”, Monash Univ., Melbourne, VIC, Australia.
Liu, F. T., Ting, K. M., and Zhou, Z. H. (2012). “Isolation-based anomaly detection.” ACM Trans. Knowl. Discovery Data, 6(1), 3.
Liu, W., and Hwang, I. (2011). “Robust estimation and fault detection and isolation algorithms for stochastic linear hybrid systems with unknown fault input.” IET Control Theory Appl., 5(12), 1353–1368.
Naganathan, H., Chong, W. O., and Chen, X. (2016). “Building energy modeling (BEM) using clustering algorithms and semi-supervised machine learning approaches.” Autom. Constr., 72, 187–194.
Nian, K., Zhang, H., Tayal, A., Coleman, T., and Li, Y. (2016). “Auto insurance fraud detection using unsupervised spectral ranking for the anomaly.” J. Finance Data Sci., 2(1), 58–75.
Poderico, M., Morani, G., and Corraro, F. (2014). “Fault detection isolation and reconfiguration algorithms for atmospheric re-entry.” 22nd Mediterranean Conf. on Control and Automation (MED), IEEE, New York, 1273–1280.
Retzlaff, R. C. (2009). “The use of LEED in planning and development regulation: An exploratory analysis.” J. Plann. Edu. Res., 29(1), 67–77.
Richards, J. (2012). “Green building—A retrospective on the history of LEED certification.” Institute for Environmental Entrepreneurship, Berkeley, CA.
Rodrigues, F., Duarte, J., Figueiredo, V., Vale, Z., and Cordeiro, M. (2003). “A comparative analysis of clustering algorithms applied to load profiling.” Int. Workshop on Machine Learning and Data Mining in Pattern Recognition, Springer, Berlin, 73–85.
Sapienza, A., Panisson, A., Wu, J., Gauvin, L., and Cattuto, C. (2015). “Anomaly detection in temporal graph data: An iterative tensor decomposition and masking approach.” Proc., 1st Int. Workshop on Advanced Analytics and Learning on Temporal Data, Portugal, 117.
Singh, P., and Singh, M. (2015). “Fraud detection by monitoring customer behavior and activities.” Int. J. Comput. Appl., 111(11), 23–32.
Sun, S., and Wang, Y. (2009). “A weighted support vector clustering algorithm and its application in network intrusion detection.” 1st Int. Workshop on Education Technology and Computer Science, ETCS’09, Vol. 1, IEEE, New York, 352–355.
Ting, K. M. (2009). “Adaptive anomaly detection using isolation forest.” Monash Univ., Churchill, VIC, Australia.
Torcellini, P., Pless, S., Deru, M., and Crawley, D. (2006). Zero energy buildings: A critical look at the definition, National Renewable Energy Laboratory and Dept. of Energy, Washington, DC.
Turner, C., and Frankel, M. (2008). “Energy performance of LEED for new construction buildings.” New Buildings Institute, Portland, OR, 1–42.
UNEP (United Nations Environmental Programme). (2009). United States green building council, buildings, and climate change: A summary for decision-makers, Sustainable Buildings and Climate Initiative, Paris, 1–62.
USEIA (U.S. Energy Information Administration). (2013). “Independent statistics and analysis.” ⟨http://www.eia.gov/consumption/commercial/reports/2012/buildstock⟩ (Apr. 25, 2016).
USEIA (U.S. Energy Information Administration). (2016). “Independent statistics and analysis.” ⟨https://www.eia.gov/consumption/commercial/reports/2012/energyusage⟩ (Apr. 27. 2016).
USGBC (U.S. Green Building Council). (2003a). “United States green building council, leadership in energy and environmental design.” ⟨http://www.usgbc.org/leed/leed_main.asp⟩ (Apr. 27, 2016).
USGBC (U.S. Green Building Council). (2003b). “United States green building council, why build green?” ⟨http://www.usgbc.org/AboutUs/whybuildgreen.asp⟩ (Apr. 27, 2016).
USGBC (U.S. Green Building Council). (2004). “United States green building council, an introduction to the United States green building council and the leed green building rating system.” ⟨https://www.usgbc.org/Docs/Resources/usgbc_intro.ppt#27⟩ (Apr. 27, 2016).
USGBC (U.S. Green Building Council). (2005). “LEED for new construction and major renovations—Version 2.2.” ⟨http://www.usgbc.org/Docs/Archive/General/Docs1095.pdf⟩ (Apr. 25, 2016).
USGBC (U.S. Green Building Council). (2007). “LEED initiatives in government by type.” ⟨https://www.usgbc.org/ShowFile.aspx?DocumentID=1741⟩ (Apr. 27, 2016).
USGBC (U.S. Green Building Council). (2009). “LEED 2009 for new construction and major renovations.” ⟨http://www.usgbc.org/Docs/Archive/General/Docs5546.pdf⟩ (Apr. 27, 2016).
Van Vlasselaer, V., et al. (2015). “APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions.” Decis. Support Syst., 75, 38–48.
Vengertsev, D., and Thakkar, H. (2015). “Anomaly detection in graph: Unsupervised learning, graph-based features, and deep architecture.” ⟨http://www.snap.stanford.edu/class/cs224w-2015/projects_2015/Anomaly_Detection_in_Graphs.pdf⟩ (Apr. 25, 2016).
Westphalen, D., and Koszalinski, S. (1999). “Energy consumption characteristics of commercial building HVAC systems. Volume II: Thermal distribution, auxiliary equipment and ventilation.”, U.S. Dept. of Energy, Washington, DC.
Wu, P., Mao, C., Wang, J., Song, Y., and Wang, X. (2016). “A decade review of the credits obtained by LEED v2. 2 certified green building projects.” Build. Environ., 102, 167–178.
Yamanishi, K., Takeuchi, J. I., Williams, G., and Milne, P. (2004). “On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms.” Data Min. Knowl. Discovery, 8(3), 275–300.
Yih, W. T., Goodman, J., and Carvalho, V. R. (2006). “Finding advertising keywords on web pages.” Proc., 15th Int. Conf. on World Wide Web, ACM, New York, 213–222.
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©2017 American Society of Civil Engineers.
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Received: Jun 14, 2016
Accepted: Apr 7, 2017
Published online: Jul 19, 2017
Published in print: Oct 1, 2017
Discussion open until: Dec 19, 2017
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