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

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. ۴۸۲ employees are selected randomly from ۳۷ 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 ۱۳ 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.

Keywords:

Energy consumption behavior , ROSETTA , Rough Set Theory , Rule induction