Coal consumption prediction in thermal power units: A feature construction and selection method

Jian Zhou and Wei Zhang

Energy, 2023, vol. 273, issue C

Abstract: Digitization and related facilities have enabled the thermal power generation enterprises to record real-time data of thermal power units. There are many data-driven applications based on real-time monitoring and operational data in power units, while limited studies lay on the operational improvements, especially on coal consumption prediction under all working conditions. We build an intelligent prediction model of coal consumption based on key features selection, working condition clustering, and regression analysis. We combine feature construction and feature selection methods to cope with the problem caused by directly specifying feature subset for model building of traditional prediction method, which may fall into the thinking pattern and miss potentially better feature subset. Besides, to cope with the different coal consumption under different working conditions, we apply cluster analysis to construct a sub-coal consumption prediction model for each cluster category. Numerical results show that compared with other methods, it has the advantages of lower regression error and moderate model complexity, which can provide efficient decision support for operational improvement in thermal power generation.

Keywords: Thermal power units; Coal consumption prediction; Regression analysis; K-means algorithm; Genetic algorithm (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:273:y:2023:i:c:s0360544223003900

DOI: 10.1016/j.energy.2023.126996

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