A Novel Volterra Prediction Model for Power System Short-Term Load Prediction

doi:10.7652/xjtuxb201409005

您当前的位置：

首页 >

文章列表页 >

A Novel Volterra Prediction Model for Power System Short-Term Load Prediction

更新时间：2025-07-09

- A Novel Volterra Prediction Model for Power System Short-Term Load Prediction
- Vol. 48, Issue 9, Pages: 24-29(2014)
- 作者机构：
  
  长安大学电子与控制工程学院,西安,710064
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201409005
  CLC： TM715
- Online First：10 September 2014，
  
  Published：2014
- 稿件说明：
移动端阅览
A Novel Volterra Prediction Model for Power System Short-Term Load Prediction[J]. 2014, 48(9): 24-29.
DOI：

A Novel Volterra Prediction Model for Power System Short-Term Load Prediction[J]. 2014, 48(9): 24-29. DOI： 10.7652/xjtuxb201409005.

摘要

针对电力系统负荷短时预测问题

从分析负荷数据的混沌特性入手

利用相空间重构理论对负荷数据进行重构

构建了一种新型的Volterra模型对电力系统负荷进行预测。该模型采用二次线性微分方程方法对原Volterra级数进行变换

与以往Volterra级数相比

该模型无截断误差

包含了系统更多精确的信息。最后

以某地区实际用电负荷数据为对象进行验证

结果表明:该模型2 d和4 d用电负荷预测结果和实际结果误差不超过5%

完全能够满足电力调度需求

同时也为电力公司制定经济模型和实时电价调整提供了理论支持。

Abstract

Focusing on short-term load forecasting in power system

a novel Volterra model is proposed. Once determining whether chaotic character exists in the load time series

the load time series is reconstructed following phase space reconstruction theory. Compared with normal Volterra model

the new Volterra model contains more accurate information of system without truncated errors. This Volterra model is used to predict a short-term load and the results show that the error between the predicted load and true load is less than 5%. The predicted short-term load is accurate enough for electric power dispatching

especially for power companies to adjust price.

关键词

Keywords

references

郑永康, 陈维荣, 蒋刚, 等. 一种模糊支持向量的负荷混沌时间序列预测法[J]. 电力系统及其自动化学报, 2007, 19(6): 48-52.

ZHENG Yongkang, CHEN Weirong, JIANG Gang, et al. Chaotic time series method for load forecasting based on fuzzy support vector[J]. Proceedings of the CSU-EPSA, 2007, 19(6): 48-52.

雷绍兰, 孙才新, 周湶, 等. 电力短期负荷的多变量时间序列线性回归预测方法研究[J]. 中国电机工程学报, 2006, 26(2): 25-29.

LEI Shaolan, SUN Caixin, ZHOU Quan, et al. The research of local linear model of short-term electrical load on multivariate time series[J]. Proceedings of the CSEE, 2006, 26(2): 25-29.

向峥嵘, 王学平. 基于小波-神经网络的电力系统短期负荷预测[J]. 系统仿真学报, 2008, 20(18): 5018-5020.

XIANG Zhengrong, WANG Xueping. Forecasting approach to short-time load using wavelet decomposition and artificial neural network[J]. Journal of System Simulation, 2008, 20(18): 5018-5020.

兰华, 常家宁, 周凌, 等. 基于局部均值分解与神经网络的短期负荷预测[J]. 电测与仪表, 2012, 49(1): 48-51.

LAN Hua, CHANG Jianing, ZHOU Ling, et al. Power system short-term load forecasting based on local mean decomposition and artificial neural network[J]. Electrical Measurement Instrumentation, 2012, 49(1): 48-51.

魏少岩, 吴俊勇. 基于灰色模型和Kalman平滑器的多母线短期负荷预测[J]. 电工技术学报, 2010, 25(2): 158-162.

WEI Shaoyan, WU Junyong. Short-term bus load forecasting based on grey model and Kalman smoother[J]. Transactions of China Electrotechnical Society, 2010, 25(2): 158-162.

冯丽, 邱家驹. 基于模糊多目标遗传优化算法的节假日电力负荷预测[J]. 中国电机工程学报, 2005, 25(10): 29-34.

FENG Li, QIU Jiaju. Short-term load forecasting for anomalous days based on fuzzy multi-objective genetic optimization algorithm[J]. Proceedings of the CSEE, 2005, 25(10): 29-34.

谢宏, 魏江平, 刘鹤立. 短期负荷预测中支持向量机模型的参数选取和优化方法[J]. 中国电机工程学报, 2006, 26(22): 17-22.

XIE Hong, WEI Jiangping, LIU Heli. Parameter selection and optimization method of SVM model for short-term load forecasting[J]. Proceedings of the CSEE, 2006, 26(22): 17-22.

蔡金锭, 付中云. 粒子群神经网络混合算法在负荷预测中的应用[J]. 高电压技术, 2007, 33(5): 90-93.

CAI Jinding, FU Zhongyun. Application of particle group and neural network hybrid algorithm in load forecast[J]. High Voltage Engineering, 2007, 33(5): 90-93.

岳毅宏, 韩文秀, 张伟波. 基于关联度的混沌序列局域加权线性回归预测法[J]. 中国电机工程学报, 2004, 24(11): 19-22.

YUE Yihong, HAN Wenxiu, ZHANG Weibo. Local adding-weight linear regression forecasting method of chaotic series based on degree of incidence[J]. Proceedings of the CSEE, 2004, 24(11): 19-22.

杜杰, 曹一家, 刘志坚, 等. 混沌时间序列的局域高阶Volterra滤波器多步预测模型[J]. 物理学报, 2009, 58(9): 5997-6005.

DU Jie, CAO Yijia, LIU Zhijian, et al. Local higher-order Volterra filter multi-step predication model for chaotic time series[J]. Acta Physica Sinica, 2009, 58(9): 5997-6005.

GU Chenjie. A projection-based nonlinear model order reduction approach using quadratic-linear representation of nonlinear systems[J]. IEEE Transactions On Computer Aided Design of Integrated Circuits and Systems, 2011, 30(9): 1307-1320.

吕金虎, 陆君安, 陈士华. 混沌时间序列分析及其应用[M]. 武汉: 武汉大学出版社, 2002.

李鹤, 杨周, 张义民, 等. 基于径向基神经网络预测的混沌时间序列嵌入维数估计方法[J]. 物理学报, 2011, 60(7): 70512.

LI He, YANG Zhou, ZHANG Yimin, et al. Methodology of estimating the embedding dimension in chaos time series based on the prediction performance of radial basis function neural networks[J]. Acta Physica Sinica, 2011, 60(7): 70512.

张学清, 梁军. 风电功率时间序列混沌特性分析及预测模型研究[J]. 物理学报, 2012, 61(19): 190507.

ZHANG Xueqing, LIANG Jun. Chaotic characteristics analysis and prediction model study on wind power time series[J]. Acta Physica Sinica, 2012, 61(19): 190507.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

An Online Transient Stability Control Strategy Based on Post-Disturbance Equilibrium Point Continuation: Calculation of Post-Disturbance Equilibrium Points

Optimal Location and Staged Installation of Phasor Measurement Unit for Complete Observability

Design for New Bridge High Temperature Superconducting Fault Current Limiter

Fast Search for Islanding Strategy Based on Self-Adaptive System Islanding

Diffusion Characteristics and Influencing Factors in Case of Hydrogen Leakage from Fuel Cell Vehicles Running in Tunnels

Related Author

DUAN Chao

GAO Qin

LI Shiyang

LIU Yuming

HE Jingjie

ZHANG Jianxin

LI Chongtao

刘东晖 2

Related Institution

School of Electrical Engineering, Xi'an Jiaotong University

Power Dispatching and Control Center, China Southern Power Grid

Electric Power Research Institute, China Southern Power Grid

State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University

⁰