Using Rough Set Theory to Analyze the Energy Audit Reports of Buildings

Rough set theory is a new mathematical approach to analyze the imperfect knowledge. It does not need any preliminary or additional information about data and provides efficient methods, algorithms and tools for finding hidden patterns in uncertain data. In this study, RST has been used to extract the rules from the data of energy audits of buildings. Since part of building energy audit data related to assessment of occupants comfort level and other data related to the technical analysis of the buildings so in this research, a decision attribute and eleven conditional attributes have been selected and rules inference have been done using ROSETTA software. Due to the different algorithms of data complement, discretization, reduction and rule generation, four rule models have been constructed based on the conditions of this study. Cross validation is used for evaluation of the model results. Finally the best model was chosen with fourteen rules and 99.8 percent of accuracy. The model demonstrate that the core attribute of buildings is "uncontrolled area of buildings". It means that if the value of this attribute is calculated the 14 rules can be used to accurately predict the level of employees comfort in the buildings.