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Title: Intelligent load pattern modeling and denoising using improved variational mode decomposition for various calendar periods

Abstract

This paper proposes a novel approach to identify the load patterns considering calendar impact and denoising process, which advances the state of art of load identification and load forecasting to a more flexible and adaptive extent by revealing the correlation between load profile and the substantial influence factors. With the rapid development and the promising application of energy intemet and grid modernization, the accurate load modeling and forecasting especially by load pattern modeling (LPM) have become more and more critical to observe and estimate load profile so as to optimize the operation efficiency of the power grid. Traditional methods mostly focus on the static LPM itself which are unable to account the non-linear and highly dynamic load fluctuations in order to balance the demand of the power grid during various calendar period (VCP) scheduling. In this paper, a load pattern modeling method for power grid considering the impact of typical calendar effects is proposed using a combination of improved variational mode decomposition (IVMD) and deep belief network (DBN) algorithm. Firstly, an index of load curve curvature (LCC) is proposed to improve the selection of value K in the conventional VMD algorithm. The value K of the intrinsic modality is determinedmore » by adopting LCC to better quantify the average instantaneous frequency of the modal decomposition of the VMD and to reduce the elevated stochasticity of k. Secondly, an optimization formulation is developed based on adaptive segmented loop decomposition of the typical correlation coefficient to suppress the edge effect in VMD decomposition, which would enable the reconstructed curve to incorporate the variabilities from the initial data. Finally, the IVMD and DBN algorithms are jointly implemented to optimize the accuracy of VCP load pattern modeling curve regression. Numerical examples have been performed with the results proving that the proposed method significantly outperforms the state-of-art in reducing the modeling error of VCP load curve, improving the overall forecasting accuracy, and reducing load dispatching cost.« less

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
Natural Science Foundation of Liaoning Province
OSTI Identifier:
1576582
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article
Journal Name:
Applied Energy
Additional Journal Information:
Journal Volume: 247
Country of Publication:
United States
Language:
English
Subject:
deep belief network; demand management; load forecasting; load pattern modeling; noise reduction; variational mode decomposition

Citation Formats

Cui, Jia, Yu, Renzhe, Zhao, Dongbo, Yang, Junyou, Ge, Weichun, and Zhou, Xiaoming. Intelligent load pattern modeling and denoising using improved variational mode decomposition for various calendar periods. United States: N. p., 2019. Web. doi:10.1016/j.apenergy.2019.03.163.
Cui, Jia, Yu, Renzhe, Zhao, Dongbo, Yang, Junyou, Ge, Weichun, & Zhou, Xiaoming. Intelligent load pattern modeling and denoising using improved variational mode decomposition for various calendar periods. United States. doi:10.1016/j.apenergy.2019.03.163.
Cui, Jia, Yu, Renzhe, Zhao, Dongbo, Yang, Junyou, Ge, Weichun, and Zhou, Xiaoming. Thu . "Intelligent load pattern modeling and denoising using improved variational mode decomposition for various calendar periods". United States. doi:10.1016/j.apenergy.2019.03.163.
@article{osti_1576582,
title = {Intelligent load pattern modeling and denoising using improved variational mode decomposition for various calendar periods},
author = {Cui, Jia and Yu, Renzhe and Zhao, Dongbo and Yang, Junyou and Ge, Weichun and Zhou, Xiaoming},
abstractNote = {This paper proposes a novel approach to identify the load patterns considering calendar impact and denoising process, which advances the state of art of load identification and load forecasting to a more flexible and adaptive extent by revealing the correlation between load profile and the substantial influence factors. With the rapid development and the promising application of energy intemet and grid modernization, the accurate load modeling and forecasting especially by load pattern modeling (LPM) have become more and more critical to observe and estimate load profile so as to optimize the operation efficiency of the power grid. Traditional methods mostly focus on the static LPM itself which are unable to account the non-linear and highly dynamic load fluctuations in order to balance the demand of the power grid during various calendar period (VCP) scheduling. In this paper, a load pattern modeling method for power grid considering the impact of typical calendar effects is proposed using a combination of improved variational mode decomposition (IVMD) and deep belief network (DBN) algorithm. Firstly, an index of load curve curvature (LCC) is proposed to improve the selection of value K in the conventional VMD algorithm. The value K of the intrinsic modality is determined by adopting LCC to better quantify the average instantaneous frequency of the modal decomposition of the VMD and to reduce the elevated stochasticity of k. Secondly, an optimization formulation is developed based on adaptive segmented loop decomposition of the typical correlation coefficient to suppress the edge effect in VMD decomposition, which would enable the reconstructed curve to incorporate the variabilities from the initial data. Finally, the IVMD and DBN algorithms are jointly implemented to optimize the accuracy of VCP load pattern modeling curve regression. Numerical examples have been performed with the results proving that the proposed method significantly outperforms the state-of-art in reducing the modeling error of VCP load curve, improving the overall forecasting accuracy, and reducing load dispatching cost.},
doi = {10.1016/j.apenergy.2019.03.163},
journal = {Applied Energy},
number = ,
volume = 247,
place = {United States},
year = {2019},
month = {8}
}