Short-term load prediction model combining FEW and IHS algorithm

• Autorzy: Yu Mingxing , Zhu Jiazheng , Yang Li

Identyfikatory

DOI

10.24425/aee.2019.130691

• Języki publikacji: EN

Abstrakty

EN

:Accurate prediction of power load plays a crucial role in the power industry and provides economic operation decisions for the power operation department. Due to the unpredictability and periodicity of power load, an improved method to deal with complex nonlinear relation was adopted, and a short-term load forecasting model combining FEW (fuzzy exponential weighting) and IHS (improved harmonic search) algorithms was proposed. Firstly, the domain space was defined, the harmony memory base was initialized, and the fuzzy logic relation was identified. Then the optimal interval length was calculated using the training sample data, and local and global optimum were updated by optimization criteria and judging criteria. Finally, the optimized parameters obtained by an IHS algorithm were applied to the FEW model and the load data of the Huludao region (2013) in Northeast China in May. The accuracy of the proposed model was verified using an evaluation criterion as the fitness function. The results of error analysis show that the model can effectively predict short-term power load data and has high stability and accuracy, which provides a reference for application of short-term prediction in other industrial fields.

Słowa kluczowe

EN

evaluation criteria; exponential fuzzy time series; fitness function; improved harmony search algorithms; load forecasting; optimal interval length

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2019
• Tom: Vol. 68, nr 4
• Strony: 907--923
• Opis fizyczny: Bibliogr. 32 poz., rys., tab., wz.

Twórcy

autor

Yu Mingxing

• Department of Information Engineering, Chaoyang Teachers College 122000, Chaoyang, P.R. China

autor

Zhu Jiazheng

• Huludao Power Supply Company, State Grid Liaoning Electrical Power Co., Ltd. 125001, Huludao, P.R. China

autor

Yang Li

• School of Equipment Engineering, Shenyang Ligong University 110159, Shenyang, P.R. China

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).