Analyzing Energy Consumption of Organizational Buildings Using Grey Set Theory

Document Type : Research Paper



In particular, by identifying clusters of Individuals, households, organizations, cities, countries and nationalities with similar behavioural patterns, it can assist in the crafting of more effective interventions and incentives targeted to specific energy cultures. it also helps energy supply companies understand different behavioural clusters among their customers, so as to better tailor their tariff schemes and products. The purpose of this paper is clustering of buildings by using Grey Set Theory. This theory has the advantage of using fewer data to analyze many factors, and it is therefore more appropriate for this study rather than traditional statistical regression which requires massive data, normal distribution in the data and few variant factors. Gray clustering in this study has been used for two purposes. First, all the variables of building relate to energy audit cluster in two main groups of indicators and the number of variables is reduced. Second, Grey Clustering with Variable Weights has been used to classify all buildings in three categories named “standard”, “Moderate standard deviation” and “completely non-standard”. This classification can be the basis of behavioral research on each group and understanding of cultural differences in each cluster, regardless of technological and structural differences between the buildings. In addition it can be as a tool for understanding the potentials and possibilities for sites of action to achieve behaviour change, whether these are at a general policy level, or targeted at a specific group


Barr, S., Gilg, A.W. & Ford, N. (2005). The household energy gap: examining the divide between habitual- and purchase-related conservation behaviours. Energy Policy, 33(11): 1425-1444.
Encinas, N. & Alfonso, D. (2007). Energy market segmentation for distributed energy resources implementation purposes, IET Generation Transmission & Distribution, 1 (2): 324-330.
Filippin, C., Larsen, S.F. & Mercado, V. (2011). Winter energy behaviour in multi-family block buildings in a temperate-cold climate in Argentina. Renewable and Sustainable Energy Reviews, 15(1): 203-219.
International Energy Conservation Code (2006). International code council, Inc.
Jian, L., Liu, S. & Lin, Y. (2011). Hybrid Rough Sets and Applications in Uncertain Decision-Making, by Taylor and Francis Group, LLC.
Jiang, W., Zhong, X., Qi, J. & Zhu, C. (2007). Grey Rough Sets Hybrid Scheme for Intelligent Fault Diagnosis. IEEE International Conference on Grey Systems and Intelligent Services, November 18-20, Nanjing, China.
Li, G.D., Yamaguchi, D. & Lin, H.S. (2006). The simulation modeling about the developments of GDP, population and primary energy consumption in china based on MATLAB. In: Proceedings of the IEEE International Conference on Cybernetics and Intelligent Systems (CIS 2006), Bangkok, Thailand, June 2006, pp 499–504.
Liu, S. & Lin, Y. (2006). Grey Information Theory and Practical Applications. Springer-Verlag London Limited.
Liu, S. & Lin, Y. (2010). Grey Systems Theory and Applications. Springer-Verlag Berlin Heidelberg.
Liu, S., Forrest, J. & Vallee, R. (2009). Emergence and development of grey systems theory. Kybernetes, 38(7/8): 1246-1256.
Michalik, G. & Mielczarski, W. (1996). Modeling of Energy Use Patterns in the Residential Sector Using Linguistic Variables. 8th International Conference on Intelligent Systems applications to Power Systems, Orlando, Florida, USA: 1996, pp. 278-282.
Raaij, W.V. & Verhallen, M.M. (1983). A Behavioral Model of Residential Energy Use. Journal of Economic Psychology, 3(1): 39-63.
Stephenson, J., Barton, B., Carrington, G., Gnoth, D., Lawson, R. & Thorsnes, P. (2010). Energy cultures: A framework for understanding energy behaviours. Energy Policy, 38(10): 6120-6129.
Wang, Q. (2009). Grey Prediction Model and Multivariate Statistical Techniques Forecasting Electrical Energy Consumption in Wenzhou, China. Intelligent Information Technology and Security Informatics. IITSI’09. Second International Symposium, pp. 167–170.
Wang, Q., Xia, F. & Wang, X. (2009). Integration of Grey Model and Multiple Regression Model to Predict Energy Consumption. Proceedings of the International Conference on Energy and Environment Technology (ICEET '09); October 2009; Guilin, China. pp. 194–197.
Xie, Y. & Li, M. (2009). Research on Prediction Model of Natural Gas Consumption Based on Grey Modeling Optimized by Genetic Algorithm. IITA International Conference on Control, Automation and Systems Engineering. 335–337. Article number 5194459.
Yu, Z., Fung, C.M., Haghighat, F., Yoshino, H. & Morofsky, E. (2011). A systematic procedure to study the influence of occupant behavior on building energy consumption. Energy and Buildings, 43(6): 1409–1417.