Temporal knowledge discovery in big BAS data for building energy management

Energy and Buildings

Volumn 109, Issue , 2015, Pages 75-89

  Fan, Cheng ^a   Xiao, Fu ^a   Madsen, Henrik ^b   Wang, Dan ^a  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

^b TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

Author keywords

Big data; Building automation system; Building energy management; Temporal knowledge discovery; Time series data mining

Indexed keywords

ASSOCIATION RULES; AUTOMATION; BUILDINGS; DATA MINING; DRUG PRODUCTS PLANTS; ENERGY MANAGEMENT; ENTERPRISE RESOURCE PLANNING; HISTORIC PRESERVATION; INFORMATION MANAGEMENT; INTELLIGENT BUILDINGS; TIME SERIES;

BUILDING AUTOMATION SYSTEMS; BUILDING ENERGY MANAGEMENTS; MULTIVARIATE TIME SERIES; OPERATIONAL PERFORMANCE; SYMBOLIC AGGREGATE APPROXIMATION (SAX); TEMPORAL ASSOCIATION RULE; TEMPORAL ASSOCIATION RULE MININGS; TIME SERIES DATA MINING;

BIG DATA;

EID: 84944755202     PISSN: 03787788     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.enbuild.2015.09.060     Document Type: Article

References (48)

1. L. Perez-Lombard, J. Ortiz, and C. Pout A review on buildings energy consumption information Energy Build. 40 2008 394 398
2. United Nations Environment Programme (UNEP) Common Carbon Metric for Measuring Energy use and Reporting Greenhouse Gas Emissions from Building Operations 2009 http://www.unep.org/sbci/pdfs/UNEPSBCICarbonMetric.pdf (accessed on 09.04.15)
3. 2 Savings in the EU Through the Use of Building Automation Technology 2013, August Final Report for the European Copper Institute
4. C. Fan, F. Xiao, and C.C. Yan A framework for knowledge discovery in massive building automation data and its applications in building diagnostics Autom. Constr. 50 2014 81 90
5. F. Dalene Technology and information management for low-carbon building J. Renew. Sustain. Energy 4 2012 041402 10.1063/1.3694120
6. B. Dong, C. Cao, and S.E. Lee Applying support vector machines to predict building energy consumption in tropical region Energy Build. 37 2005 545 553
7. A.S. Ahmad, M.Y. Hassan, M.P. Abdullah, H.A. Rahman, F. Hussin, H. Abdullah, and R. Saidur A review on applications of ANN and SVM for building electrical energy consumption forecasting Renew. Sustain. Energy Rev. 33 2014 102 109
8. C. Fan, F. Xiao, and S.W. Wang Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques Appl. Energy 127 2014 1 10
9. A. Kusiak, G.L. Xu, and Z.J. Zhang Minimization of energy consumption in HVAC systems with data-driven models and an interior-point method Energy Convers. Manag. 85 2014 146 153
10. S.K. Kwok, K.K. Yuen, and W.M. Lee An intelligent approach to assessing the effect of building occupancy on building cooling load prediction Build. Environ. 46 2011 1681 1690
11. S.M. Wu, and D. Clements-Croome Understanding the indoor environment through mining sensory data - a case study Energy Build. 39 2007 1183 1191
12. G. Kim, L. Schaefer, T.S. Lim, and J.T. Kim Thermal comfort prediction of an underfloor air distribution system in a large indoor environment Energy Build. 64 2013 323 331
13. Z. Yu, F. Haghighat, C.M. Fung, and H. Yoshino A decision tree method for building energy demand modeling Energy Build. 42 2010 1637 1646
14. J.S. Chou, Y.C. Hsu, and L.T. Lin Smart meter monitoring and data mining techniques for predicting refrigeration system performance Expert Syst. Appl. 41 2014 2144 2156
15. E.U. Kucuksille, R. Selbas, and A. Sencan Prediction of thermodynamic properties of refrigerants using data mining Energy Convers. Manag. 52 2011 836 848
16. D.F.M. Cabrera, and H. Zareipour Data association mining for identifying lighting energy waste patterns in educational institutes Energy Build. 62 2013 210 216
17. A. Capozzoli, F. Lauro, and I. Khan Fault detection analysis using data mining techniques for a cluster of smart office buildings Expert Syst. Appl. 42 2015 4324 4338
18. F. Xiao, and C. Fan Data mining in building automation system for improving building operational performance Energy Build. 75 2014 109 118
19. X. Xue, S.W. Wang, Y.J. Sun, and F. Xiao An interactive building power demand management strategy for facilitating smart grid optimization Appl. Energy 116 2014 297 310
20. A. Azadeh, M. Saberi, S.F. Ghaderi, A. Gitiforouz, and V. Ebrahimipour Improved estimation of electricity demand function by integration of fuzzy system and data mining approach Energy Convers. Manag. 49 2008 2165 2177
21. I. Fernandez, C. Borges, and Y. Penya Efficient building load forecasting Proceedings of the 16th IEEE International Conference of Emerging Technologies and Factory Automation 5-9 September, Toulouse, France 2011
22. Y. Yao, Z.W. Lian, S.Q. Liu, and Z.J. Hou Hourly cooling load prediction by a combined forecasting model based on analytic hierarchy process Int. J. Therm. Sci. 43 2004 1107 1118
23. M. Kawashima, C.E. Dorgan, and J.W. Mitchell Hourly thermal load prediction for the next 24 h by ARIMA, EWMA, LR, and an artificial neural network ASHRAE Trans. 101 1995 186 200
24. J.C.M. Yiu, and S.W. Wang A multiple ARMAX modeling scheme for forecasting of air conditioning system performance Energy Convers. Manag. 48 2007 2276 2285
25. F. Zamora-Martinez, P. Romeu, P. Botella-Rocamora, and J. Pardo Towards energy efficiency: forecasting indoor temperature via multivariate analysis Energies 6 2013 4639 4659
26. L. Renners, R. Bruns, and J. Dunkel Situation-aware energy control by combining simple sensors and complex event processing Workshop on AI Problems and Approaches for Intelligent Environment August, Montpellier, France 2012
27. Y.C. Wen, G.Y. Lin, T. Sung, M. Liang, G. Tsai, and M.W. Feng A complex event processing architecture for energy and operation management The 5th ACM International Conference on Distributed Event-Based Systems July 11-15, New York, USA 2011
28. J. O'Donnell, E. Corry, S. Hasan, M. Keane, and E. Curry Building performance optimization using cross-domain scenario modeling, linked data, and complex event processing Build. Environ. 62 2013 102 111
29. T.C. Fu A review on time series data mining Eng. Appl. Artif. Intell. 17 2011 164 181
30. H. Madsen Time Series Analysis 1st ed. 2007 Chapman & Hall/CRC Texts in Statistical Science
31. D. Patnaik, M. Marwah, R.K. Sharma, and N. Ramakrishnan Temporal data mining approaches for sustainable chiller management in data centers ACM Trans. Intell. Syst. Technol. 2 2011 1 29
32. C. Miller, Z. Nagy, and A. Schlueter Automated daily pattern filtering of measured building performance data Autom. Constr. 49 2015 1 17
33. A. Gelman, and J. Hill Data analysis using regression and multi-level/hierarchical models Analytical Methods for Social Research 1st ed. 2006 Cambridge University Press
34. M. Gupta, J. Gao, C.C. Aggarwal, and J.W. Han Outlier detection for temporal data: a survey IEEE Trans. Knowl. Data Eng. 15 2014 1 20
35. R.K. Pearson Outliers in process modeling and identification IEEE Trans. Control Syst. Technol. 10 2002 55 63
36. J. Lin, E. Keogh, L. Wei, and S. Lonardi Experiencing SAX: a novel symbolic representation of time series Data Min. Knowl. Discov. 15 2007 107 144
37. J. Kwac, J. Flora, and R. Rajagopal Household energy consumption segmentation using hourly data IEEE Trans. Smart Grid 5 2014 420 430
38. R. Gulbinas, A. Khosrowpour, and J. Taylor Segmentation and classification of commercial building occupants by energy-use efficiency and predictability IEEE Trans. Smart Grid 6 2015 1414 1424
39. C.S. Daw, C.E.A. Finney, and E.R. Tracy A review of symbolic analysis of experimental data Rev. Sci. Instrum. 74 2003 915 930
40. A.L.N. Fred, and A.K. Jain Combining multiple clustering using evidence accumulation IEEE Trans. Pattern Anal. Mach. Intell. 27 2005 835 850
41. S. Vega-Pons, and J. Ruiz-Schulcoper A survey of clustering ensemble algorithms Int. J. Pattern Recognit. Artif. Intell. 25 2011 337 372
42. B. Chiu, E. Keogh, and S. Lonardi Probabilistic Discovery of Time Series Motifs 2003 ACM SIGKDD Washington, DC, USA 493 498
43. Y. Tanaka, K. Iwamoto, and K. Uehara Discovery of time-series motif from multi-dimensional data based on MDL principle Mach. Learn. 58 2005 269 300
44. D. Minnen, C.L. Isbell, I. Essa, and T. Starner Discovering multivariate motifs using subsequence density estimation and greedy mixture learning The 22nd National Conference on Artificial Intelligence, vol. 1 2007 615 620
45. A. Vahdatpour, N. Amini, and M. Sarrafzadeh Towards unsupervised activity discovery using multi-dimensional motif detection in time series 21st International Joint Conference on Artificial Intelligence, IJCAI 2009
46. M.J. Zaki SPADE: an efficient algorithm for mining frequent sequences Mach. Learn. 42 2001 30 60
47. P. Fournier-Viger, U. Faghihi, R. Nkambou, and E.M. Nguifo CMRules: mining sequential rules common to several sequences Knowl. Based Syst. 25 2012 63 76
48. P. Fournier-Viger, C.W. Wu, V.S. Tseng, and R. Nkambou Mining sequential rules common to several sequences with the window size constraint Advances in Artificial-25th Canadian Conference on Artificial Intelligence Toronto, Canada 2012 299 304