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Application of Fuzzy Support Vector Machine in Short-Term Power Load Forecasting

Application of Fuzzy Support Vector Machine in Short-Term Power Load Forecasting

Jie Yang, Yachun Tang, Huabin Duan
Copyright: © 2022 |Volume: 24 |Issue: 5 |Pages: 10
ISSN: 1548-7717|EISSN: 1548-7725|EISBN13: 9781668453926|DOI: 10.4018/JCIT.295248
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MLA

Yang, Jie, et al. "Application of Fuzzy Support Vector Machine in Short-Term Power Load Forecasting." JCIT vol.24, no.5 2022: pp.1-10. http://doi.org/10.4018/JCIT.295248

APA

Yang, J., Tang, Y., & Duan, H. (2022). Application of Fuzzy Support Vector Machine in Short-Term Power Load Forecasting. Journal of Cases on Information Technology (JCIT), 24(5), 1-10. http://doi.org/10.4018/JCIT.295248

Chicago

Yang, Jie, Yachun Tang, and Huabin Duan. "Application of Fuzzy Support Vector Machine in Short-Term Power Load Forecasting," Journal of Cases on Information Technology (JCIT) 24, no.5: 1-10. http://doi.org/10.4018/JCIT.295248

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Abstract

The realization of short-term load forecasting is the basis of system planning and decision-making, and it is an important index to evaluate the safety and economy of power grid.In order to accurately predict the power load under the influence of many factors, a new short-term power load prediction method based on fuzzy support vector machine and similar daily linear extrapolation is proposed, which combinesthe method of fuzzy support vector machine and linear extrapolation of similar days. The method first selects similar days according to the effect of integrated weather and time on load. Then the fuzzy membership of the training sample is obtained by the normalization processing, and the daily maximum and minimum load is predicted by the fuzzy support vector machine. Finally, the load prediction value is obtained by combining the load trend curve obtained by the similar daily linear extrapolation method. and this method is feasible and effective for short-term forecasting of power load.