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A Hybrid Heat Rate Forecasting Model Using Optimized LSSVM Based on Improved GSA

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Abstract

Heat rate value is considered as one of the most important thermal economic indicators, which determines the economic, efficient and safe operation of steam turbine unit. At the same time, an accurate heat rate forecasting is core task in the optimal operation of steam turbine unit. Recently, least squares support vector machine (LSSVM) is being proved an effective machine learning technique for solving nonlinear regression problem with a small sample set. However, it has also been proved that the prediction precision of LSSVM is highly dependent on its parameters, which are hardly choosing for the LSSVM. In the paper, an improved gravitational search algorithm (AC-GSA) is presented to further enhance optimal performance of GSA, and it is employed to serve as an approach for pre-selecting LSSVM parameters. Then, a novel soft computing method, based on LSSVM and AC-GSA, is therefore proposed to forecast heat rate of a 600 MW supercritical steam turbine unit. It combines the merits of the high accuracy of LSSVM and the fast convergence of GSA in order to build heat rate prediction model and obtain a well-generalized model. Results indicate that the developed AC-GSA–LSSVM model demonstrates better regression precision and generalization capability.

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Acknowledgments

This research was supported by the National Natural Science Foundation of China (Grant nos. 61403331, 61573306), and China Postdoctoral Science Foundation (Grant No. 2015M571280), and Natural Science Foundation of Hebei Province(Grant No. F 2016203427).

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Authors and Affiliations

  1. Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, 066004, China

    Chao Liu, Peifeng Niu, Guoqiang Li & Yunpeng Ma

  2. National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, Qinhuangdao, 066004, China

    Peifeng Niu

  3. The School of Information and Electric Engineering, Panzhihua University, Panzhihua, 617000, China

    Xia You

  4. Qinhuangdao Institute of Technology, Qinhuangdao, 066100, China

    Weiping Zhang

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  1. Chao Liu
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  2. Peifeng Niu
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  4. Xia You
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  5. Yunpeng Ma
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Correspondence to Peifeng Niu.

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Liu, C., Niu, P., Li, G. et al. A Hybrid Heat Rate Forecasting Model Using Optimized LSSVM Based on Improved GSA. Neural Process Lett 45, 299–318 (2017). https://doi.org/10.1007/s11063-016-9523-0

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