Identification of Comprehensive Energy Consumption Feature Based on Rough Set Bayesian Classification Algorithm

Abstract

Mining of valuable information from user energy consumption data efficiently and accurately has always been a research hotspot in the power industry. Bayesian classification method is one of the important data processing methods in the field of machine learning and data mining research. It has the advantages of simplicity, high efficiency and stable classification effect, and it provides an effective solution to the user’s comprehensive energy consumption feature identification. A model describing energy consumption data to predict trends is built, training data set is analyzed, a classification model is constructed, and the data records in the database to a given category are maped, which can be applied to data prediction. By studying incomplete information systems, expanding rough sets, constructing extended models, Bayesian classification algorithms based on rough sets theory is designed, and user comprehensive energy consumption feature identification is realized. Experiments show that the user comprehensive energy consumption feature identification algorithm based on rough set Bayesian classification can greatly improve the classification accuracy.