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Application of Improved Least Squares Support Vector Machine in the Forecast of Daily Water Consumption

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Abstract

In order to better predict city daily water consumption to achieve the optimal scheduling of city water supply system, based on the research progress summarizing the city daily water consumption forecasting at home and abroad, we take the predicted daily water consumption main influence factors and predicted daily related water use after noise reduction as input, and the predicted daily water consumption after noise reduction as output. In addition, we adopt the multiple scale chaos genetic with strong global search capability and faster search speed to optimize the parameters of least square support vector machine. Moreover, we establish a prediction model of daily water consumption of least squares support vector machine based on wavelet multiple scale chaos genetic. The case analysis results show that the model proposed in this paper has strong prediction ability, compared with the least square support vector machine prediction model based on multiple scale chaos genetic, least square support vector machine prediction model based on wavelet, and prediction model based on genetic least square support vector machine algorithm. At last, it is concluded that the improved least square support vector machine has good performance in the application in daily water consumption prediction.

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Acknowledgements

The authors acknowledge the National Natural Science Foundation of China (Grant No. 61662045).

Funding

Funding was provided by National Natural Science Foundation of China (Grant No. 61662045) and the Science & Technology Foundation for Selected overseas Chinese scholar of Tianjin 2017.

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Author notes

  1. Weiping Zhang and Qin Yang have contributed equally to the paper as first authors.

Authors and Affiliations

  1. Department of Electronic Information Engineering, Nanchang University, Nanchang, 330031, China

    Weiping Zhang

  2. Faculty of Computer Science, Jiangxi University of Traditional Chinese Medicine, Nanchang, 330004, China

    Qin Yang

  3. Binhai Industrial Technology Research Institute of Zhejiang University, Tianjin, 300301, China

    Weiping Zhang

  4. Zhejiang University College of Civil Engineering and Architecture, Hangzhou, 310027, China

    Yihua Mao

  5. Faculty of Computer Science and Electrical Engineering, University of Rostock, 18055, Rostock, Germany

    Mohit Kumar

Authors
  1. Weiping Zhang
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  2. Qin Yang
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  3. Mohit Kumar
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  4. Yihua Mao
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Corresponding author

Correspondence to Yihua Mao.

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Zhang, W., Yang, Q., Kumar, M. et al. Application of Improved Least Squares Support Vector Machine in the Forecast of Daily Water Consumption. Wireless Pers Commun 102, 3589–3602 (2018). https://doi.org/10.1007/s11277-018-5393-2

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  • DOI: https://doi.org/10.1007/s11277-018-5393-2

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