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Open AccessArticle

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

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School of Mathematics and Statistics Science, Ping Ding Shan University, Ping Ding Shan 467000, China
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Department of Information Management, Oriental Institute of Technology/No. 58, Sec. 2, Sichuan Rd., Panchiao, New Taipei 226, Taiwan
*
Author to whom correspondence should be addressed.
Energies 2019, 12(5), 916; https://doi.org/10.3390/en12050916
Received: 11 January 2019 / Revised: 14 February 2019 / Accepted: 6 March 2019 / Published: 9 March 2019
(This article belongs to the Special Issue Intelligent Optimization Modelling in Energy Forecasting)
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. View Full-Text
Keywords: short-term load forecasting; weighted k-nearest neighbor (W-K-NN) algorithm; comparative analysis short-term load forecasting; weighted k-nearest neighbor (W-K-NN) algorithm; comparative analysis
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Fan, G.-F.; Guo, Y.-H.; Zheng, J.-M.; Hong, W.-C. Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting. Energies 2019, 12, 916.

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