Yu, Zhun (2012) Mining Hidden Knowledge from Measured Data for Improving Building Energy Performance. PhD thesis, Concordia University.
- Accepted Version
Nowadays, building automation and energy management systems provide an opportunity to collect vast amounts of building-related data (e.g., climatic data, building operational data, etc.). The data can provide abundant useful knowledge about the interactions between building energy consumption and its influencing factors. Such interactions play a crucial role in developing and implementing control strategies to improve building energy performance. However, the data is rarely analyzed and this useful knowledge is seldom extracted due to a lack of effective data analysis techniques.
In this research, data mining (classification analysis, cluster analysis, and association rule mining) is proposed to extract hidden useful knowledge from building-related data. Moreover, a data analysis process and a data mining framework are proposed, enabling building-related data to be analyzed more efficiently. The applications of the process and framework to two sets of collected data demonstrate their applicability. Based on the process and framework, four data analysis methodologies were developed and applied to the collected data.
Classification analysis was applied to develop a methodology for establishing building energy demand predictive models. To demonstrate its applicability, the methodology was applied to estimate residential building energy performance indexes by modeling building energy use intensity (EUI) levels (either high or low). The results demonstrate that the methodology can classify and predict the building energy demand levels with an accuracy of 93% for training data and 92% for test data, and identify and rank significant factors of building EUI automatically.
Cluster analysis was used to develop a methodology for examining the influences of occupant behavior on building energy consumption. The results show that the methodology facilitates the evaluation of building energy-saving potential by improving the behavior of building occupants, and provides multifaceted insights into building energy end-use patterns associated with the occupant behavior.
Association rule mining was employed to develop a methodology for examining all associations and correlations between building operational data, thereby discovering useful knowledge about energy conservation. The results show there are possibilities for saving energy by modifying the operation of mechanical ventilation systems and by repairing equipment.
Cluster analysis, classification analysis, and association rule mining were combined to formulate a methodology for identifying and improving occupant behavior in buildings. The results show that the methodology was able to identify the behavior which needs to be modified, and provide occupants with feasible recommendations so that they can make required decisions to modify their behavior.
|Divisions:||Concordia University > Faculty of Engineering and Computer Science > Building, Civil and Environmental Engineering|
|Item Type:||Thesis (PhD)|
|Degree Name:||Ph. D.|
|Date:||9 January 2012|
|Thesis Supervisor(s):||Haghighat, Fariborz and Fung, Benjamin|
|Keywords:||Building energy conservation, Data mining, Knowledge, Building energy performance improvement|
|Deposited By:||ZHUN YU|
|Deposited On:||20 Jun 2012 17:42|
|Last Modified:||20 Jun 2012 17:42|
AboulNaga, M.M., Elsheshtawy, Y.H. (2001). Environmental sustainability assessment of buildings in hot climates: the case of the UAE. Renewable Energy, 24, (3-4), 553-563.
Al-ajmi, F.F., Hanby, V.I. (2008). Simulation of energy consumption for Kuwaiti domestic buildings. Energy and Buildings, 40, (6), 1101-1109.
Anstett, M., Kreider, J.F. (1993). Application of neuronal network models to predict energy use. ASHRAE Transactions 99. Part 1.
Al-Mumin, A., Khattab, O., Sridhar, G. (2003). Occupants' behavior and activity patterns influencing the energy consumption in the Kuwaiti residences. Energy and Buildings, 35, (6), 549–559.
Andersson, S., Olofsson T., Ostin, R. (1996). Predictions of energy demand in buildings using neural network techniques on performance data. Proceedings of the 4th Symposium on Building Physics in the Nordic Countries, Espoo, Finland.
Aydinalp, M., Ugursal, V.I., Fung, A.S. (2002). Modelling of the appliance, lighting, and space cooling energy consumption in the residential sector using neural networks. Applied Energy, 71, (2), 87-110.
Bourgeois, D. (2005). Detailed occupancy prediction, occupancy-sensing control and advanced behavioral modeling within whole-building energy simulation. Ph.D. thesis, l’Universite Laval, Quebec.
Bouckaert, R.R. et al. (2009). WEKA Manual for Version 3-7-0. University of Waikato. New Zealand.
Balaras, C.A., Droutsa, K., Argiriou, A.A., Wittchen, K. (2002). Assessment of energy and natural resources conservation in office buildings using TOBUS. Energy and Buildings 34, (2), 135-153.
Balaras, C.A., Dascalaki, E., Gaglia, A., Droutsa, K. (2003). Energy conservation potential, HVAC installations and operational issues in Hellenic airports. Energy and Buildings 35, (11), 1105-1120.
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J. (1984). Classification and Regression Trees, Wadsworth, Inc., California.
Balaras, C.A., Gaglia, A.G. Georgopoulou, E., Mirasgedis, S., Sarafidis, Y., Lalas, D.P. (2007). European residential buildings and empirical assessment of the Hellenic building stock, energy consumption, emissions and potential energy savings. Building and Environment, 42, (3), 1298-1314.
Caldera, M., Corgnati, S.P., Filippi, M. (2008). Energy demand for space heating through a statistical approach: application to residential buildings. Energy and Buildings, 40, (10), 1972-1983.
Chow, T.T., Fong, K.F., He, W., Lin, Z., Chan, A.L.S. (2007). Performance evaluation of a PV ventilated window applying to office building of Hong Kong. Energy and Buildings 39, (6), 643-650.
Chung, W., Hui, Y.V. (2009). A study of energy efficiency of private office buildings in Hong Kong. Energy and Buildings, 41, (6), 696-701.
Chung, W., Hui., Y.V. (2009). A study of energy efficiency of private office buildings in Hong Kong. Energy and Buildings, 41, (6), 696-701.
Cabena, P., Hadjinian, P., Stadler, R., Verhees, J., Zanasi, A. (1998). Discovering data mining: from concept to implementation, Prentice Hall, Upper Saddle River, NJ.
Climate Statistics, Japan Meteorological Agency. Monthly Mean and Monthly Total Tables: http://www.data.jma.go.jp/obd/stats/data/en/smp/index.html
Crawley, D.B., Lawrie, L.K., Winkelmann, F.C., Buhl, W.F., Huang, Y.J., Pedersen, C.O., Strand, R.K., Liesen, R.J., Fisher, D.E., Witte, M.J., Glazer, J. (2001). EnergyPlus: creating a new-generation building energy simulation program. Energy and Buildings 33, (4), 319-331.
Cios, K.J., Pedrycz, W., Swiniarski, R.W. (2007). Data mining: a knowledge discovery approach, Springer, New York.
Catalina, T., Virgone, J., Blanco, E. (2008). Development and validation of regression models to predict monthly heating demand for residential buildings. Energy and Buildings, 40, (10), 1825-1832.
Cai, W.G., Wu, Y., Zhong, Y., Ren, H. (2009). China building energy consumption: Situation, challenges and corresponding measures. Energy Policy, 37, (6), 2054-2059.
Chen, S., Yoshino, H., Li, N. (2009). Statistical analyses on summer energy consumption characteristics of residential buildings in some cities of China. Energy and Buildings, 42, (1), 136-146.
Chen, S., Yoshino, H., Li, N. (2010). Statistical analyses on summer energy consumption characteristics of residential buildings in some cities of China. Energy and Buildings, 42, (1), 136-146.
Chen, S., Yoshino, H., Levine, M.D., Li, Z. (2009). Contrastive analyses on annual energy consumption characteristics and the influence mechanism between new and old residential buildings in Shanghai, China, by the statistical methods. Energy and Buildings, 41, (12), 1347-1359.
Cao, L.B., Yu, P.S., Zhang, C.Q., Zhang, H.F. (2009). Data mining for business applications, Springer, New York.
Deng, J.L. (1989). Introduction to grey system. Journal of Grey System, 1, 1-24.
Deng, S. (2003). Energy and water uses and their performance explanatory indicators in hotels in Hong Kong. Energy and Buildings, 35, (8), 775-784.
Davies, D.L., Bouldin, D.W. (1979). A Cluster Separation Measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2, 224.
Deng, S.M., Burnett, J. (2000). A study of energy performance of hotel buildings in Hong Kong. Energy and Buildings, 31, (1), 7-12.
Dong, B., Cao, C., Lee, S.E. (2005). Applying support vector machines to predict building energy consumption in tropical region. Energy and Buildings 37, (5), 545-553.
De la Flor, F.J.S. Lissén, J.M.S., Domínguez, S.Á. (2006). A new methodology towards determining building performance under modified outdoor conditions. Building and Environment, 41, (9), 1231-1238.
Dong, B., Lee, S.E., Sapar, M.H. (2005). A holistic utility bill analysis method for baselining whole commercial building energy consumption in Singapore. Energy and Buildings, 37, (2), 167-174.
Delgado, M., Sánchez, D.M., MartIn-Bautista, J., Vila, M.-A. (2001) Mining association rules with improved semantics in medical databases. Artificial Intelligence in Medicine 21, (1-3), 241-245.
Ekici, B.B., Aksoy, U.T. (2009). Prediction of building energy consumption by using artificial neural networks. Advances in Engineering Software, 40, (5), 356-362.
Emery, A.F., Kippenhan, C.J. (2006). A long-term study of residential home heating consumption and the effect of occupant behavior on homes in the Pacific Northwest constructed according to improved thermal standards. Energy, 31, (5), 677-693.
Eskin, N., Türkmen, H. (2008). Analysis of annual heating and cooling energy requirements for office buildings in different climates in Turkey. Energy and Buildings 40, (5), 763-773.
Frank, E., Hall, M., Trigg, L., Holmes, G., Witten, I.H. (2004). Data Mining in Bioinformatics using Weka, Bioinformatics Advance Access. Oxford University Press, England.
Filippín, C.S., Larsen, Flores. (2009). Analysis of energy consumption patterns in multi-family housing in a moderate cold climate. Energy Policy, 37, (9), 3489-3501.
Freire, R.Z., Oliveira, G.H.C., Mendes, N. (2008). Development of regression equations for predicting energy and hygrothermal performance of buildings. Energy and Buildings 40, (5), 810-820.
Fu, C., Zheng, J., Zhao, J., Xu, W. (2001). Application of grey relational analysis for corrosion failure of oil tubes. Corrosion Science, 43, (5), 881-889.
Gaunt, L. (1985). Habits and energy. Meddelande M85:14, The National Swedish Institute for Building Research, Sweden.
Ghiaus, C. (2006). Experimental estimation of building energy performance by robust regression. Energy and Buildings, 38, (6), 582-587.
Georgilakis, P.S., Gioulekas, A.T., Souflaris., A.T. (2007). A decision tree method for the selection of winding material in power transformers. Journal of Materials Processing Technology, 181, (1-3), 281-285.
Givoni, B., Kruger, E.L. (2003). An attempt to base prediction of indoor temperatures of occupied houses on their thermo-physical properties. Proceeding of the Eighteenth International Passive and Low Energy Architecture Conference (PLEA’03), Santiago, Chile.
Gaitani, N., Lehmann, C., Santamouris, M., Mihalakakou, G., Patargias, P. (2010). Using principal component and cluster analysis in the heating evaluation of the school building sector. Applied Energy, 87, (6), 2079-2086.
Hammarsten, S. (1979). A survey of Swedish buildings from the energy aspect. Energy and Buildings, 2, (2), 125-134.
Helsel D.R., (2002). Hirsch R.M. Statistical methods in water resources. U.S. department of the interior. U.S.
Hein, K.R.G. (2005). Future energy supply in Europe--challenge and chances. Fuel, 84, (10), 1189-1194.
Hsu, C.H. (2009). Data mining to improve industrial standards and enhance production and marketing: An empirical study in apparel industry. Expert Systems with Applications 36, (3), 4185-4191.
Hoes, P., Hensen, J.L.M., Loomans, M.G.L.C., De Vries, B., Bourgeois, D. (2009). User behavior in whole building simulation. Energy and Buildings, 41, (3), 295-302.
Han, J.W., Kamber, M. (2006). Data Mining Concepts and Techniques 2nd ed.), Elsevier Inc., San Francisco.
Hand, D., Mannila, H., Smyth, P. (2001). Principles of data mining, MIT press, Cambridge, MA.
HTML 4 Common Attributes: http://htmlhelp.com/reference/html40/attrs.html
Instruction for WEKA: http://weka.wikispaces.com/Primer
Jiménez, M.J., Heras, M.R. (2005). Application of multi-output ARX models for estimation of the U and g values of building components in outdoor testing. Solar Energy 79, (3), 302-310.
Jiao, J., Zhang, Y. (2005). Product portfolio identification based on association rule mining. Computer-Aided Design, 37, (2), 149-172.
Kawashima, M. (1994). Artificial neural network back propagation model with three-phase annealing developed for the building energy predictor shootout. ASHRAE Transactions 100, Part 2.
Kreider, J.F., Claridge, D.E., Curtiss, P., Dodier, R., Haberl J.S., Krati, M. (1995). Building energy use prediction and system identification using recurrent neural networks, Journal of Solar Energy Engineering, 117, 161-166.
Krüger, E., Givoni, B. (2004). Predicting thermal performance in occupied dwellings. Energy and Buildings, 36, (3), 301-307.
Kim, Y.S., Kim, K.S. (2007). Simplified energy prediction method accounting for part-load performance of chiller. Building and Environment 42, (1), 507-515.
Kreider, J.F., Wang, X.A. (1992). Improved artificial neural networks for commercial building energy use prediction. Proceedings of the ASME Annual Solar Engineering Meeting, Maui, HI.
Kreider, J.F., Wang, X.A. (1997). Artificial neural network demonstration for automated generation of energy use predictors for commercial buildings. ASHRAE Transactions 97, Part 2.
Lam, J.C. (2000). Energy analysis of commercial buildings in subtropical climates. Building and Environment, 35, (1), 19-26.
Lazzarin, R.M., Castellotti, F., Busato, F. (2005). Experimental measurements and numerical modeling of a green roof. Energy and Buildings, 37, (12), 1260-1267.
Lopes, L., Hokoi, S., Miura, H., Shuhei, K. (2005). Energy efficiency and energy savings in Japanese residential buildings – research methodology and surveyed results, Energy and Buildings, 37, (7), 698-706.
Lam, J.C., Hui, S.C.M., Chan, A.L.S. 1997. Regression analysis of high-rise fully air-conditioned office buildings. Energy and Buildings, 26, (2), 189-197.
Li, Q., Meng, Q., Cai, J., Yoshino, H., Mochida, A. (2009). Applying support vector machine to predict hourly cooling load in the building. Applied Energy, 86, (10), 2249-2256.
Li, H., Nalim, R., Haldi, P.A. (2006). Thermal-economic optimization of a distributed multi-generation energy system--A case study of Beijing. Applied Thermal Engineering 26, (7), 709-719.
Lam, J.C., Wan, K.K.W., Cheung, K.L. (2009). An analysis of climatic influences on chiller plant electricity consumption. Applied Energy, 86, (6), 933-940.
Li, Y.M., Wu, J.Y., Shiochi, S. (2010). Experimental validation of the simulation module of the water-cooled variable refrigerant flow system under cooling operation. Applied Energy , 87, (5): 1513-1521.
Long, E.S., Zhou, J. (2005). Classified identifications: the annual relative variation rates (RVRs) of energy consumption are approximate in different cities with the same shading coefficient. Building and Environment 40, (4), 517-528.
Murakami, S., Akabayashi, S., Inoue, T., Yoshino, H., Hasegawa, K., Yuasa, K., Ikaga, T. (2006). Energy consumption for residential buildings in Japan, Architectural Institute of Japan, Maruzen Corp., http://tkkankyo.eng.niigata-u.ac.jp/HP/HP/database/index.htm.
Monts, J.K., Blissett, M. (1982). Assessing energy efficiency and energy conservation potential among commercial buildings: A statistical approach. Energy, 7, (10), 861-869.
MIT Technology review. (2001). Emerging Technologies That Will Change the World.
M. Aydinalp, V.I. Ugursal and A.S. Fung. Modeling of the space and domestic hot water energy consumption in the residential sector using neural networks. Applied Energy 2004; 79 (2): 159-178.
MacDonald, J.M., Wasserman, D.M. (1989). Investigation of metered data analysis methods for commercial and related buildings. Report to U.S. Department of Energy under contract No. DE-AC05-84OR21400, Oak Ridge National Laboratory, Oak Ridge, Tennessee.
Nakagami, H. (1996). Lifestyle change and energy use in Japan: Household equipment and energy consumption. Energy, 21, (12), 1157-1167.
Olofsson, T., Andersson, S. (2001). Long-term energy demand predictions based on short-term measured data. Energy and Buildings, 33, (2), 85-91.
Ourghi, R., Al-Anzi, A., Krarti, M. (2007). A simplified analysis method to predict the impact of shape on annual energy use for office buildings. Energy Conversion and Management 48, (1), 300-305.
O’Neill, P.J., Crawley, D.B., Schliesing, J.S. (1991). Using regression equations to determine the relative importance of inputs to energy simulation tools. Proceedings of the Building Simulation ‘91 Conference Sophia-Antipolis, Nice, France, 1991.
Ouyang, J., Hokao, K. (2009). Energy-saving potential by improving occupants' behavior in urban residential sector in Hangzhou City, China, Energy and Buildings, 41, (7), 711-720.
Ordenes, M., Marinoski, D.L., Braun, P., Rüther, R. (2007). The impact of building-integrated photovoltaics on the energy demand of multi-family dwellings in Brazil. Energy and Buildings 39, (6), 629-642.
Pan, H., Li, J., Zhang, W. (2007). Incorporating domain knowledge into medical image clustering. Applied Mathematics and Computation, 185, (2), 844-856.
Pérez-Lombard, L., Ortiz, J., Coronel, J. F., Maestre, I.R. (2011). A review of HVAC systems requirements in building energy regulations. Energy and Buildings, 43, (2-3), 255-268.
Pérez-Lombard, L., Ortiz, J., Pout, C. (2008). A review on buildings energy consumption information. Energy and Buildings, 40, (3), 394-398.
Priyadarsini, R., Wu, X.C., Lee, S.E. (2009). A study on energy performance of hotel buildings in Singapore. Energy and Buildings, 41, (12), 1319-1324.
Pan, Y., Yin, R., Huang, Z. (2008). Energy modeling of two office buildings with data center for green building design. Energy and Buildings 40, (7), 1145-1152.
Quinlan, J.R. (1986). Induction of decision trees. Machine Learning, 1, 81-106.
Quinlan, J.R. (1993). C4.5 Programs for Machine Learning, Morgan Kaufmann, San Mateo.
Reinhart, C.F. (2004). Lightswitch-2002: a model for manual and automated control of electric lighting and blinds. Solar Energy 77, (1), 15-28.
Rokach, L., Maimon, O. (2008). Data mining with decision trees: theory and applications, SG: World Scientific, Singapore.
Rijal, H.B., Tuohy, P., Humphreys, M.A., Nicol, J.F., Samuel, A., Clarke, J. (2007). Using results from field surveys to predict the effect of open windows on thermal comfort and energy use in buildings. Energy and Buildings 39, (7), 823-836.
Shannon, C.E. (1948). A mathematical theory of communication. The Bell System Technical J., 27, 379-623.
Standby Power, Frequently Asked Questions (FAQs): http://standby.lbl.gov/faq.html
Santamouris, M., Balaras, C.A., Dascalaki, E., Argiriou, A., Gaglia, A.
Repository Staff Only: item control page
Downloads per month over past year