Dynamic classification system in large-scale supervision of energy efficiency in buildings

Data mining and knowledge discovery applied to the billing data provide the diagnostic instruments for the evaluation of energy use in buildings connected to a district heating network. To ensure the validity of an algorithm-based classification system, the dynamic properties of a sequence of partitions for consecutive detected events were investigated. The information regarding the dynamic properties of the classification system refers to the similarities between the supervised objects and migrations that originate from the changes in the building energy use and loss similarity to their neighbourhood and thus represents the refinement of knowledge. In this study, we demonstrate that algorithm-based diagnostic knowledge has dynamic properties that can be exploited with a rough set predictor to evaluate whether the implementation of classification for supervision of energy use aligns with the dynamics of changes of district heating-supplied building properties. Moreover, we demonstrate the refinement of the current knowledge with the previous findings and we present the creation of predictive diagnostic systems based on knowledge dynamics with a satisfactory level of classification errors, even for non-stationary data.