Joern Ploennigs
ABSTRACT
Buildings consume 40% of the energy in industrialized countries. Thus detecting and diagnosing anomalies in the building's energy use is an important problem. The existing approaches either retrieve limited information about the anomaly causes, or are difficult to adapt to different buildings. This paper presents an easily adaptable diagnosis approach that exploits the building's hierarchy of submeters, i. e. information on how much energy is used by the different building equipments. It computes novel diagnosis results consisting of two parts: (i) the extent to which building equipments cause abnormal energy use, and (ii) the extent to which internal and external factors determine the energy use of building equipments. Computing such diagnosis results requires an approach that can predict the energy use for the different submeters and that can also determine the factors that influence the energy use. However, existing building approaches do not meet these requirements. As a remedy, we propose a novel approach using the generalized additive model (GAM), which incorporates various exogenous variables affecting building energy use, such as weather conditions and time of the day. Our experiments demonstrate that the proposed method can efficiently model the impact of different factors and diagnose the causes of anomalies.
- Y. Agarwal, T. Weng, and R. K. Gupta. The energy dashboard: improving the visibility of energy consumption at a campus-wide scale. In 1st ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, BuildSys '09, page 55--60, New York, NY, USA, 2009. ACM. Google Scholar
Digital Library
- G. Bellala, M. Marwah, M. Arlitt, G. Lyon, and C. Bash. Following the electrons: methods for power management in commercial buildings. In 18th ACM SIGKDD Int. Conf. on Knowledge discovery and data mining, KDD '12, page 994--1002, New York, NY, USA, 2012. Google ScholarDigital Library
- G. Bellala, M. Marwah, M. Arlitt, G. Lyon, and C. E. Bash. Towards an understanding of campus-scale power consumption. In 3rd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, BuildSys '11, page 73--78, New York, NY, USA, 2011. Google ScholarDigital Library
- A. Dhar, T. A. Reddy, and D. E. Claridge. Modeling hourly energy use in commercial buildings with fourier series functional forms. Journal of Solar Energy Engineering, 120(3), Aug. 1998.Google ScholarCross Ref
- J. Duchon. Splines minimizing rotation-invariant semi-norms in sobolev spaces. In W. Schempp and K. Zeller, editors, Constructive Theory of Functions of Several Variables, volume 571 of Lecture Notes in Mathematics, pages 85--100. Springer, 1977.Google ScholarCross Ref
- P. A. González and J. M. Zamarreño. Prediction of hourly energy consumption in buildings based on a feedback artificial neural network. Energy and Buildings, 37(6):595--601, June 2005.Google ScholarCross Ref
- M. C. Hao, H. Janetzko, S. Mittelstädt,W. Hill, U. Dayal, D. A. Keim, M. Marwah, and R. K. Sharma. A visual analytics approach for peak-preserving prediction of large seasonal time series. Computer Graphics Forum, 30(3):691--700, 2011. Google ScholarDigital Library
- International Energy Agency. World energy outlook 2012, 2012.Google Scholar
- V. Jakkula and D. Cook. Outlier detection in smart environment structured power datasets. In IE - 6th Int. Conf. on Intelligent Environments, pages 29--33, 2010. Google ScholarDigital Library
- S. Katipamula and M. Brambley. Methods for fault detection, diagnostics, and prognostics for building systems - a review, part I. HVAC&R Research, 11(1):3--25, 2005.Google ScholarCross Ref
- Q. Li, Q. Meng, J. Cai, H. Yoshino, and A. Mochida. Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks. Energy Conversion and Management, 50(1):90--96, Jan. 2009.Google ScholarCross Ref
- F. Liu, H. Jiang, Y. M. Lee, J. Snowdon, and M. Bobker. Statistical modeling for anomaly detection, forecasting and root cause analysis of energy consumption for a portfolio of buildings. BS - Building Simulation, 2011.Google Scholar
- M. Misiti, Y. Misiti, G. Oppenheim, and J. Poggi. Optimized clusters for disaggregated electricity load forecasting. REVSTAT, 8:105--124, 2010.Google Scholar
- A. H. Neto and F. A. S. Fiorelli. Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption. Energy and Buildings, 40(12):2169--2176, 2008.Google ScholarCross Ref
- O. Perpiñán. solaR: Solar radiation and photovoltaic systems with R. Journal of Statistical Software, 50(9):1--32, 2012.Google ScholarCross Ref
- J. E. Seem. Using intelligent data analysis to detect abnormal energy consumption in buildings. Energy and Buildings, 39(1):52--58, Jan. 2007.Google ScholarCross Ref
- S. Shaw, S. Leeb, L. Norford, and R. Cox. Nonintrusive load monitoring and diagnostics in power systems. IEEE Trans. Instrum. Meas., 57(7):1445--1454, 2008.Google ScholarCross Ref
- R. Tibshirani and T. Hastie. Generalized additive models. Chapman & Hall, London, 1990.Google Scholar
- S.Wood. Generalized additive models: An Introduction with R. Chapman & Hall, London, 2006. Google ScholarDigital Library
- H. Zhao and F. Magoulès. Parallel support vector machines applied to the prediction of multiple buildings energy consumption. Journal of Algorithms & Computational Technology, 4(2):231--249, June 2010.Google ScholarCross Ref
Index Terms
- Exploiting Generalized Additive Models for Diagnosing Abnormal Energy Use in Buildings
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