The energy use analysis of coal-fired power plant units is of significance for energy conservation and consumption reduction. One of the most serious problems attributed to Chinese coal-fired power plants is coal waste. Several units in one plant may experience a practical rated output situation at the same time, which may increase the coal consumption of the power plant. Here, we propose a new hybrid methodology for plant-level load optimization to minimize coal consumption for coal-fired power plants. The proposed methodology includes two parts. One part determines the reference value of the controllable operating parameters of net coal consumption under typical load conditions, based on an improved K-means algorithm and the Hadoop platform. The other part utilizes a support vector machine to determine the sensitivity coefficients of various operating parameters for the net coal consumption under different load conditions. Additionally, the fuzzy rough set attribute reduction method was employed to obtain the minimalist properties reduction method parameters to reduce the complexity of the dataset. This work is based on continuously-measured information system data from a 600 MW coal-fired power plant in China. The results show that the proposed strategy achieves high energy conservation performance. Taking the 600 MW load optimization value as an example, the optimized power supply coal consumption is 307.95 g/(kW·h) compared to the actual operating value of 313.45 g/(kW·h). It is important for coal-fired power plants to reduce their coal consumption.

There is a big amount of overlap among the data of thermal system, which will seriously affect the accuracy and precision of the model. In order to improve the value density of big data of thermal system and improve the quality and efficiency of modeling, pretreatment and analysis have to be made. Aimed at the diagnosis and treatment of the multiple co-linearity among variables, three reduction models have been established, respectively by Pearson correlation coefficient diagnostic method, VIF auxiliary regression test and condition number diagnosis method. And the models have been validated by the data of a 600MW unit of a power plant. Through the analysis of the results, it is found that the condition number diagnosis method can effectively solve the problem of multiple co-linearity of big data of thermal system, realizing the pretreatment of big data of thermal system.