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

Guo-Feng Fan, Yan-Hui Guo, Jia-Mei Zheng and Wei-Chiang Hong
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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

Energies, 2019, vol. 12, issue 5, 1-19

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.

Keywords: short-term load forecasting; weighted k-nearest neighbor (W-K-NN) algorithm; comparative analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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