ABSTRACT
A typical large building contains thousands of sensors, monitoring the HVAC system, lighting, and other operational sub-systems. With the increased push for operational efficiency, operators are relying more on historical data processing to uncover opportunities for energy-savings. However, they are overwhelmed with the deluge of data and seek more efficient ways to identify potential problems. In this paper, we present a new approach called the Strip, Bind and Search (SBS); a method for uncovering abnormal equipment behavior and in-concert usage patterns. SBS uncovers relationships between devices and constructs a model for their usage pattern relative to other devices. It then flags deviations from the model. We run SBS on a set of building sensor traces; each containing hundred sensors reporting data flows over 18 weeks from two separate buildings with fundamentally different infrastructures. We demonstrate that, in many cases, SBS uncovers misbehavior corresponding to inefficient device usage that leads to energy waste. The average waste uncovered is as high as 2500~kWh per device.
- Y. Agarwal, B. Balaji, S. Dutta, R. K. Gupta, and T. Weng. Duty-cycling buildings aggressively: The next frontier in hvac control. In IPSN'11, pages 246--257, Chicago, IL, USA, 2011.Google Scholar
- G. Bellala, M. Marwah, M. Arlitt, G. Lyon, and C. E. Bash. Towards an understanding of campus-scale power consumption. Buildsys'11, page 6, Seattle, WA, Nov. 1, 2011. Google ScholarDigital Library
- G. Bellala, M. Marwah, A. Shah, M. Arlitt, and C. Bash. A finite state machine-based characterization of building entities for monitoring and control. pages 153--160, 2012. Google ScholarDigital Library
- M. Blanco-Velasco, B. Weng, and K. E. Barner. Ecg signal denoising and baseline wander correction based on the empirical mode decomposition. Computers in biology and medicine, 38(1):1--13, jan. 2008. Google ScholarDigital Library
- V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre. Fast unfolding of communities in large networks. J.STAT.MECH., 2008.Google Scholar
- M. Brown, C. Barrington-Leigh, and Z. Brown. Kernel regression for real-time building energy analysis. Journal of Building Performance Simulation, 5(4):263--276, 2012.Google ScholarCross Ref
- P. Chan, M. Mahoney, and M. Arshad. Learning rules and clusters for anomaly detection in network traffic. In Managing Cyber Threats, volume 5 of Massive Computing, pages 81--99. Springer US, 2005.Google ScholarCross Ref
- C. Chen and D. J. Cook. Energy outlier detection in smart environments. In Artificial Intelligence and Smarter Living, volume WS-11-07 of AAAI Workshops. AAAI, 2011.Google Scholar
- V. L. Erickson, M. A. Carreira-Perpinan, and A. Cerpa. Observe: Occupancy-based system for efficient reduction of hvac energy. In IPSN'11, pages 258--269, Chicago, IL, USA, 2011.Google Scholar
- R. Fontugne, J. Ortiz, D. Culler, and H. Esaki. Empirical mode decomposition for intrinsic-relationship extraction in large sensor deployments. In IoT-App'12, Workshop on Internet of Things Applications, Beijing, China, 2012.Google Scholar
- T. Hasan and M. Hasan. Suppression of residual noise from speech signals using empirical mode decomposition. Signal Processing Letters, IEEE, 16(1):2--5, jan. 2009.Google ScholarCross Ref
- H. Huang and J. Pan. Speech pitch determination based on hilbert-huang transform. Signal Processing, 86(4):792 -- 803, 2006. Google ScholarDigital Library
- N. E. Huang. Computing frequency by using generalized zero-crossing applied to intrinsic mode functions. U.S. Patent 6,990,436 B1, 2006.Google Scholar
- N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung, and H. H. Liu. The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A, 454(1971):903--995, 1998.Google ScholarCross Ref
- N. E. Huang, Z. Wu, S. R. Long, K. C. Arnold, X. Chen, and K. Blank. On instantaneous frequency. Advances in Adaptive Data Analysis, pages 177--229, 2009.Google ScholarCross Ref
- P. Huber and E. Ronchetti. Robust Statistics. Wiley Series in Probability and Statistics. Wiley, 2009.Google Scholar
- S. Katipamula and M. Brambley. Review article: Methods for fault detection, diagnostics, and prognostics for building systems a A Ta review, part i. HVAC&R Research, 11(1):3--25, 2005.Google ScholarCross Ref
- S. Katipamula and M. Brambley. Review article: Methods for fault detection, diagnostics, and prognostics for building systems a A Ta review, part ii. HVAC&R Research, 11(2):169--187, 2005.Google ScholarCross Ref
- Y. Kim, R. Balani, H. Zhao, and M. B. Srivastava. Granger causality analysis on ip traffic and circuit-level energy monitoring. BuildSys'10, pages 43--48, Zurich, Switzerland, Nov. 2, 2010. Google ScholarDigital Library
- T. Lee and T. B. M. J. Ouarda. Prediction of climate nonstationary oscillation processes with empirical mode decomposition. Journal of Geophysical Research, 116, 2011.Google Scholar
- J. C. Nunes, S. Guyot, and E. Delechelle. Texture analysis based on local analysis of the bidimensional empirical mode decomposition. Machine Vision and Applications, 16:177--188, 2005.Google ScholarDigital Library
- D. Patnaik, M. Marwah, R. Sharma, and N. Ramakrishnan. Temporal data mining approaches for sustainable chiller management in data centers. ACM Transactions on Intelligent Systems and Technology, 2(4), 2011. Google ScholarDigital Library
- J. Schein and S. Bushby. A hierarchical rule-based fault detection and diagnostic method for hvac systems. HVAC&R Research, 12(1):111--125, 2006.Google ScholarCross Ref
- J. E. Seem. Using intelligent data analysis to detect abnormal energy consumption in buildings. Energy and Buildings, 39(1):52 -- 58, 2007.Google ScholarCross Ref
- M. Torres, M. Colominas, G. Schlotthauer, and P. Flandrin. A complete ensemble empirical mode decomposition with adaptive noise. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 4144--4147, May 2011.Google ScholarCross Ref
- U.S. Energy Information Administration. Annual Energy Review 2011, 2012.Google Scholar
- M. Wrinch, T. H. EL-Fouly, and S. Wong. Anomaly detection of building systems using energy demand frequency domain anlaysis. In IEEE Power & Energy Society General Meeting, San-Diego, CA, USA, 2012.Google Scholar
- Q. Zhou, S. Wang, and Z. Ma. A model-based fault detection and diagnosis strategy for hvac systems. International Journal of Energy Research, 33(10):903--918, 2009.Google ScholarCross Ref
Index Terms
- Strip, bind, and search: a method for identifying abnormal energy consumption in buildings
Recommendations
Management Review of Energy Consumption: The Energy Saving Opportunity in University Buildings
ICIBE '19: Proceedings of the 5th International Conference on Industrial and Business EngineeringThe building is identified as one of the largest electricity user. The reduction of energy consumption in the building sector has the considerable opportunity and the significant impact on the growth of energy demand, consequently, it would reduce the ...
Energy Consumption Data Based Machine Anomaly Detection
CBD '14: Proceedings of the 2014 Second International Conference on Advanced Cloud and Big DataThe ever increasing of product development and the scarcity of the energy resources that those manufacturing activities heavily rely on have made it of great significance the study on how to improve the energy efficiency in manufacturing environment. ...
Energy-efficient data collection in strip-based wireless sensor networks with optimal speed mobile data collectors
AbstractEnergy consumption and network lifetime are the major concerns in wireless sensor networks (WSNs). In particular, WSNs use radios for communication, which are the major energy consumers. Due to frequent data forwarding process, the ...
Comments