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Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting

Author

Listed:
  • Guo-Feng Fan

    (School of Mathematics and Statistics Science, Ping Ding Shan University, Ping Ding Shan 467000, China)

  • Yan-Hui Guo

    (School of Mathematics and Statistics Science, Ping Ding Shan University, Ping Ding Shan 467000, China)

  • Jia-Mei Zheng

    (School of Mathematics and Statistics Science, Ping Ding Shan University, Ping Ding Shan 467000, China)

  • Wei-Chiang Hong

    (Department of Information Management, Oriental Institute of Technology/No. 58, Sec. 2, Sichuan Rd., Panchiao, New Taipei 226, Taiwan)

Abstract

In this paper, the historical power load data from the National Electricity Market (Australia) is used to analyze the characteristics and regulations of electricity (the average value of every eight hours). Then, considering the inverse of Euclidean distance as the weight, this paper proposes a novel short-term load forecasting model based on the weighted k-nearest neighbor algorithm to receive higher satisfied accuracy. In addition, the forecasting errors are compared with the back-propagation neural network model and the autoregressive moving average model. The comparison results demonstrate that the proposed forecasting model could reflect variation trend and has good fitting ability in short-term load forecasting.

Suggested Citation

  • Guo-Feng Fan & Yan-Hui Guo & Jia-Mei Zheng & Wei-Chiang Hong, 2019. "Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting," Energies, MDPI, vol. 12(5), pages 1-19, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:5:p:916-:d:212457
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    References listed on IDEAS

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