Desining an Expert System for Analyzing the Energy Consumption Behavior of Employees in Organizations Using Rough Set Theory

Document Type : Research Paper

Author

Assistance Prof., Farabi Campuse, University of Tehran, Tehran, Iran

Abstract

Understanding and changing the energy consumption behavior requires extensive knowledge about the motives of behavior. In this research, Rough Set Theory is used to investigate the energy consumption behavior of employees in organizations. So, thirteen condition attributes and a decision attribute are selected and the decision system is created. Condition attributes include demographic, values, attitudes and organizational characteristics of employees and decision attribute relates to energy consumption behavior. 482 employees are selected randomly from 37 office buildings of ministry of Petroleum and rough modeling are performed for them. By combining different methods of discretizing, reduction algorithms and rule generating, nine models are made using ROSETTA software. The results show that four of the 13 condition attributes, involving “organizational citizenship”, “satisfaction”, “attitude toward behavior” and “lighting control” are selected as the main features of the system. After cross validation of the various models, the model of manually discretizing using genetic algorithms and ORR approach to extract reducts has the most accuracy and selected as the most reliable model.

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References

Abraham, A., Falcon, R. & Bello, R. (2009). Rough Set Theory: A True Landmark in Data Analysis. Springer-Verlag Berlin Heidelberg.
Abrahamse, W., Steg, L., Vlek, C., Rothengatter, T. (2005). A review of intervention studies aimed at household energy conservation. Journal of Environmental Psychology, 25(3): 273–291.
Aydinalp-Koksal, M., Ugursal, V.I. (2008). Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector. Applied Energy, (85): 271–296.
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):1425–1444.
Clarke, E.J. & Barton, B.A. (2000). Entropy and MDL Discretization of Continuous Variables for Bayesian Belief Networks. International Journal of Intelligent systems, 15 (1): 61-92.
Faiers, A., Cook, M. & Neame, C. (2007). Towards a contemporary approach for understanding consumer behaviour in the context of domestic energy use. Energy Policy, (35): 4381-4390.
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): 203-219.
Funches, V., Markley, M. & Davis, L. (2009). Reprisal, retribution and requital: Investigating customer retaliation. Journal of Business Research, (62): 231–238.
Gaojun, L. & Yan, Z. (2006). Credit Assessment of Contractors: A Rough Set Method, Tsinghua Science and Technology, 11 (3): 357-362.
Goldschmidt, N. & Chung, B.G. (2001). Size does matter: the effect of organizational size on customer satisfaction. Journal of Quality Management, 6 (1): 47–60.
Grzymala-Busse, J.W. & Stefanowski, J. (2001). Three Discretization Methods for Rule Induction, International Journal of Intelligent systems, 16 (1): 29-38.
Gyberg, P. & Palm, J. (2009). Influencing households’ energy behaviour—how is this done and on what premises? Energy Policy,(37): 2807–2813.
Haas, R., Auer, H. & Biermayr, P. (1998). The impact of consumer behavior on residential energy demand for space heating. Energy and Buildings, (27): 195-205.
Henryson, J., Hakansson, T. & Pyrko, J. (2000). Energy efficiency in buildings through information – Swedish perspective. Energy Policy, (28): 169-180.
Hitchcock, G. (1993). An integrated framework for energy use and behaviour in the domestic sector. Energy and Buildings, 20 (2): 151-157.
Jian, L., Liu, S. & Lin, Y. (2011). Hybrid Rough Sets and Applications in Uncertain Decision-Making. Taylor and Francis Group, LLC.
Karimi, T. & Sadeghi Moghadam, M. (2014). Rough Set and Grey Set Theory: principles, applications and software's, First edition, Tehran: Mehraban Nashr. (in Persian)
Linden, A.L, Carlsson-Kanyama, A. & Eriksson, B. (2006). Efficient and inefficient aspects of residential energy behavior: What are the policy instruments for change? Energy Policy, (34): 1918-1927.
Lingyun, M., Rui, N., Hualong, L. & Xiaohua, L. (2011). Empirical Research of Social Norms Affecting Urban Residents Low Carbon Energy Consumption Behavior. Energy Procedia, (5): 229–234.
Ludl, M.C. & Widmer, G. (2000). Relative Unsupervised Discretization for Association Rule Mining. Fourth European Conf. Principles of Data Mining and Knowledge Discovery (PKDD), pp. 148-158.
McCalley, L.T. & Midden, J.H. (2002). Energy conservation through product-integrated feedback: The roles of goal-setting and social orientation. Journal of Economic Psychology, (23): 589–603.
Papakostas, K.T. & Sotiropoulos, B.A. (1997). Occupational and energy behaviour patterns in Greek residences. Energy and Buildings, (26): 207-213.
Starr, M.A. (2009). The social economics of ethical consumption: Theoretical considerations and empirical evidence. The Journal of Socio-Economics (38): 916–925.
Starzyk, J. A., Nelson, D. E. & Sturtz, K. (2000). A Mathematical Foundation for Improved Reduct Generation in Information Systems. Journal of Knowledge and Information Systems, 2 (2):131-146.
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.
Yan, S. & Lifang, F. (2011). Influence of psychological, family and contextual factors on residential energy use behaviour: An empirical study of China. Energy Procedia, (5): 910–915.
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): 1409-1417.
Zhong, N., Dong, J. & Ohsuga, S. (2001). Using Rough Sets with Heuristics for Feature Selection. Journal of Intelligent Information Systems, 16 (3): 199-214.
 
 
 

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