Forecasting of load model based on typical daily load profile and BP neural network

Rongsen Zhang; Guigang Qi; Canbing Li; Long Li; Yiping Bao; Yusheng Zhu

doi:10.1117/12.2014031

13 March 2013 Forecasting of load model based on typical daily load profile and BP neural network

Rongsen Zhang, Guigang Qi, Canbing Li, Long Li, Yiping Bao, Yusheng Zhu

Proceedings Volume 8784, Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies; 87841G (2013) https://doi.org/10.1117/12.2014031
Event: Fifth International Conference on Machine Vision (ICMV 12), 2012, Wuhan, China

Abstract

Load modeling is recognized as a difficult issue in field of power system digital simulation. The reliability of the simulation results depends on the veracity of the load model which will further affect power system planning and aid decision making. In order to increase the accuracy of the load model, the composite loads of power consuming-industries were classified by their industry attributes and the components of them were also analyzed in this paper. Then, the mathematical model of load composition is established on the basic of typical daily load profile and the identification algorithm developed by C language is used to identify the parameters of composite loads by choosing the data collected during the corresponding characteristic time period of the typical day. Based on the model vector machine theory and the parameters identified, the parameters of composite load model of power consuming-industries can be calculated by using the way of least square approximation. And the BP neural network was used to forecast the parameters of composite loads of power consuming-industries. Finally, an example shows the validity of the proposed scheme.

Citation Download Citation

Rongsen Zhang, Guigang Qi, Canbing Li, Long Li, Yiping Bao, and Yusheng Zhu "Forecasting of load model based on typical daily load profile and BP neural network", Proc. SPIE 8784, Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies, 87841G (13 March 2013); https://doi.org/10.1117/12.2014031

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