基于多尺度时序建模与估计的电力负荷数据恢复

doi:10.19595/j.cnki.1000-6753.tces.191664

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Abstract Aiming at the defects and distortions of power load data, this paper presents a method of load data completion and recovery from the perspective of processing and characteristic analysis, modeling and estimation of time series data. Markov chain analysis and sequential Monte Carlo simulation methods were used to extract load statistics characteristics, then based on the power load fluctuation characteristics analysis of year, month, week and day, the multi-scale time-series characteristics of the load were modeled. B-spline basis function expansion method was introduced to solve the problems caused by non-parametric and variable coefficients characteristics of the load model, and an estimation method of the key parameters in the load model was provided. The optimal number of B-spline nodes and the optimal number of spline splines were determined by the multi-index error evaluation method. Based on the known load recovery model, a weekly lost power load recovery method is proposed, and the idea of daily load data recovery for the whole year or long duration is obtained. The method proposed in this paper is proved to be accurate and effective by practical examples and is of considerable engineering application value.

Key words： Power load recovery extraction of load statistics characteristics multi-scale time-series modeling degree of B-spline evaluating indicator of error

Received: 28 November 2019

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	Zhang Shuai
	Yang Jingxian
	Liu Jichun
	Liu Junyong
	Lin Huazhen

Cite this article:

Zhang Shuai,Yang Jingxian,Liu Jichun等. Power Load Recovery Based on Multi-Scale Time-Series Modeling and Estimation[J]. Transactions of China Electrotechnical Society, 2020, 35(13): 2736-2746.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.191664 OR https://dgjsxb.ces-transaction.com/EN/Y2020/V35/I13/2736

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