Key energy-consumption feature selection of thermal power systems based on robust attribute reduction with rough sets

Abstract

Mechanism analysis is the main method for energy-consumption feature selection of thermal power systems. Although this method can select a few features with specific physical meanings as the input variables for energy-consumption modeling, it may ignore some important attributes which affect the fitting and generalization capabilities of the model. We propose a robust attribute reduction method with rough set theory and apply it to the above task. Taking a boiler combustion system of some 600MW power unit as experimental subject, an energy-consumption model with the selected key features is built. Several comparative experiments were carried out on the operational data to evaluate the performance of the energy-consumption model. The experimental results show that the robust attribute reduction algorithm has strong generalization ability, and the energy-consumption model built with key features has a strong fitting ability and high prediction accuracy, thus providing an effective method for energy-consumption prediction and optimization of the thermal power system.