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Guo-Feng Fan
School of Mathematics and Statistics, Ping Ding Shan University, Ping Ding Shan 467000, Henan, China

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Journal article
Published: 21 March 2019 in Energies
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For operational management of power plants, it is desirable to possess more precise short-term load forecasting results to guarantee the power supply and load dispatch. The empirical mode decomposition (EMD) method and the particle swarm optimization (PSO) algorithm have been successfully hybridized with the support vector regression (SVR) to produce satisfactory forecasting performance in previous studies. Decomposed intrinsic mode functions (IMFs), could be further defined as three items: item A contains the random term and the middle term; item B contains the middle term and the trend (residual) term, and item C contains the middle terms only, where the random term represents the high-frequency part of the electric load data, the middle term represents the multiple-frequency part, and the trend term represents the low-frequency part. These three items would be modeled separately by the SVR-PSO model, and the final forecasting results could be calculated as A+B-C (the defined item D). Consequently, this paper proposes a novel electric load forecasting model, namely H-EMD-SVR-PSO model, by hybridizing these three defined items to improve the forecasting accuracy. Based on electric load data from the Australian electricity market, the experimental results demonstrate that the proposed H-EMD-SVR-PSO model receives more satisfied forecasting performance than other compared models.

ACS Style

Wei-Chiang Hong; Guo-Feng Fan. Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting. Energies 2019, 12, 1093 .

AMA Style

Wei-Chiang Hong, Guo-Feng Fan. Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting. Energies. 2019; 12 (6):1093.

Chicago/Turabian Style

Wei-Chiang Hong; Guo-Feng Fan. 2019. "Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting." Energies 12, no. 6: 1093.

Journal article
Published: 09 March 2019 in Energies
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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.

ACS Style

Guo-Feng Fan; Yan-Hui Guo; Jia-Mei Zheng; Wei-Chiang Hong. Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting. Energies 2019, 12, 916 .

AMA Style

Guo-Feng Fan, Yan-Hui Guo, Jia-Mei Zheng, Wei-Chiang Hong. Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting. Energies. 2019; 12 (5):916.

Chicago/Turabian Style

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 12, no. 5: 916.