Ultra Short-term Power Load Forecasting Based on Randomly Distributive Embedded Framework and BP Neural Network

被引：0
|
作者：
Li G. [1 ]
Liu Z. [1 ]
Jin G. [1 ]
Quan R. [1 ]
机构：
[1] Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Northeast Electric Power University, Ministry of Education, Jilin, 132012, Jilin Province
来源：
Dianwang Jishu/Power System Technology | 2020年 / 44卷 / 02期
关键词：
BP neural network; Nonlinear dynamical systems; Randomly distributive embedded framework; Short-term data; Ultra short-term load forecasting;
D O I：
10.13335/j.1000-3673.pst.2019.1612
中图分类号：
学科分类号：
摘要：
Easily affected by the combined various factors such as weather or holidays, it is difficult for the ultra short-term load forecasting in power systems to achieve accurate results. In order to improve the prediction precision, a large amount of historical data is required for training.Aiming at the newly-built initial power systems with less historical data, an ultra short-term power load prediction method based on a randomly distributive embedded framework and BP neural network is presented. Firstly,the delay variables of different state variables such as power load variables and meteorological variables in the power system are considered as an independent factor.So different sets of delay variables are trained and predicted with BP neutral network algorithm,and multiple prediction values are obtained.Then,the kernel density estimation method is used to fit multiple predicted values to form the probability density function of the distribution.Finally, the final predicted value of power load is calculated by expectation estimation or aggregation estimation. The simulation results of case analysis done with the actual load data show that the proposed method is suitable for ultra short-term load forecasting with less training data, and thatit has higher prediction accuracy and stability than several conventional forecasting algorithms. © 2020, Power System Technology Press. All right reserved.
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页码：437 / 444
页数：7
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