ISSN 0253-2778

CN 34-1054/N

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Open AccessOpen Access JUSTC Original Paper

An EMD-based method for assessing and analyzing coal consumption of coal-fired units

  • Jiangsu Frontier Electric Technology CO.LTD, Nanjing 211102, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2015.10.004
  • Received Date: 12 September 2014
  • Accepted Date: 29 December 2014
  • Rev Recd Date: 29 December 2014
  • Publish Date: 30 October 2015
    Abstract

  • Abstract

    Operation habits of coal-fired unit workers often affect significantly the level of coal consumption. In practice, different operation teams usually consume different amounts of coal, thus it is necessary to carry out benchmarking management to guide the workers towards optimal operations. Conventionally, the level of coal consumption within a time period is evaluated by statistics such as the mean, the minima, and the maxima. However, these simple statistics are not informative enough to reveal the operation habits of workers. an EMD-based assessment method is proposed to analyze operation habits of teams. By taking the coal consumption distribution of the best operation team (i.e., the one with minimal amount of total coal consumption) as the baseline comparator, the method first estimates, for non-optimal teams, how far away their operations are from the optimum, and then finds out the primary parameters that cause the difference. Based on such an analysis, suggestions are provided to the operation teams, such that their behaviors can be adjusted. The proposed method achieves accurate assessment of coal consumption and may provide an analytical method for electric power enterprises. to save energy and reduce coal consumption. And it also may provide technical support for government supervision departments to carry out fine benchmarking management and performance evaluation of energy-saving for coal-fired units.

    Abstract

    Operation habits of coal-fired unit workers often affect significantly the level of coal consumption. In practice, different operation teams usually consume different amounts of coal, thus it is necessary to carry out benchmarking management to guide the workers towards optimal operations. Conventionally, the level of coal consumption within a time period is evaluated by statistics such as the mean, the minima, and the maxima. However, these simple statistics are not informative enough to reveal the operation habits of workers. an EMD-based assessment method is proposed to analyze operation habits of teams. By taking the coal consumption distribution of the best operation team (i.e., the one with minimal amount of total coal consumption) as the baseline comparator, the method first estimates, for non-optimal teams, how far away their operations are from the optimum, and then finds out the primary parameters that cause the difference. Based on such an analysis, suggestions are provided to the operation teams, such that their behaviors can be adjusted. The proposed method achieves accurate assessment of coal consumption and may provide an analytical method for electric power enterprises. to save energy and reduce coal consumption. And it also may provide technical support for government supervision departments to carry out fine benchmarking management and performance evaluation of energy-saving for coal-fired units.
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    • References(9)
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    [1]
    许震. 基于KL距离的半监督分类算法[D]. 上海: 复旦大学, 2010.
    [2]
    李中魁. 基于动态阈值的网络流量异常检测方法研究与实现[D]. 成都: 电子科技大学, 2010.
    [3]
    李开灿. Pearson-χ2距离的若干性质[J]. 数学的实践与认识, 2003, 33(1): 49-53.
    LiK C. On the properties of the distance of Pearson-χ2[J]. Mathematics in Practice and theory, 2003, 33(1): 49-53.
    [4]
    于春蕾. 基于非参数统计的判别分析[D]. 济南: 山东大学, 2013.
    [5]
    牛君. 基于非参数密度估计点样本分析建模的应用研究[D]. 济南: 山东大学, 2007.
    [6]
    Rubner Y, Tomasi C, Guibas L J. The Earth Mover’s distance as a metric for image retrieval[J]. International Journal of Computer Vision, 2000, 40(2): 99-121.
    [7]
    Rubner Y, Tomasi C, Guibas L J. A metric for distributions with applications to image databases[C] // Proceedings of the International Conference on Computer Vision. Bombay, India: Narosa Publishing House, 1998: 59-66.
    [8]
    Hillier F S, Lieberman G J. Introduction to Mathematical Programming[M]. New York: McGraw-Hill, 1990.
    [9]
    Ling H B, Okada K. An efficient earth mover's distance algorithm for robust histogram comparison[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(5): 840-853.)
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