基于负荷特性聚类及Elastic Net分析的短期负荷预测方法

doi:10.11930/j.issn.1004-9649.201905111

中国电力 ›› 2020, Vol. 53 ›› Issue (9): 221-228.DOI: 10.11930/j.issn.1004-9649.201905111

• 电网 • 上一篇

基于负荷特性聚类及Elastic Net分析的短期负荷预测方法

靳冰洁, 林勇, 罗澍忻, 韦斌, 周姝灿

广东电网有限责任公司电网规划研究中心，广东广州 510080

收稿日期:2019-05-24 修回日期:2019-11-13 发布日期:2020-09-09
作者简介:靳冰洁(1989—)，女，工程师，从事电力系统运行与规划研究，E-mail: loveice_2008@126.com;林勇(1973—)，男，硕士研究生，从事电力系统规划与分析研究，E-mail: linyong@gd.csg.cn;罗澍忻(1987—)，男，博士，工程师，从事电力系统分析与规划研究，E-mail: 707423786@qq.com
基金资助:
南方电网规划专题项目(030000QQ00180023)

A Short-Term Load Forecasting Method Based on Load Curve Clustering and Elastic Net Analysis

JIN Bingjie, LIN Yong, LUO Shuxin, WEI Bin, ZHOU Shucan

Power Grid Planning Center of Guangdong Power Grid Company, Guangzhou 510080, China

Received:2019-05-24 Revised:2019-11-13 Published:2020-09-09
Supported by:
This work is supported by South China Power Grid Planning Project (No.030000QQ00180023)

摘要/Abstract

摘要： 提出了一种基于负荷特性聚类及Elastic Net分析的短期负荷预测方法。通过对历史负荷特性进行分析和聚类，对全年日进行分类并指定日类型，避免日类型选择过于宽泛且缺乏针对性。同时采用Elastic Net方法对影响负荷预测的主导因素进行辨识和筛选。最后，在以上预测输入变量优化的基础上，建立神经网络预测模型。以广东省某市实际负荷为例，通过与其他方法对比，验证了所提方法在提高日负荷曲线预测精度方面的有效性。算例结果表明，所提模型适用期较长，无须反复训练，对短期负荷预测有较强的应用价值。

关键词: 负荷特性, 聚类分析, 弹性网络, 神经网络, 负荷预测

Abstract: A short-term load forecasting method based on load characteristics clustering and elastic net analysis is proposed in this paper. By analyzing and clustering the historical load characteristics, the annual days are classified and its clusters are specified, and the lack of pertinence of the types of the day cluster selection is avoided. At the same time, Elastic net analysis is adopted to identify and select the dominant factors for short-term load forecasting. Furthermore, the neural network forecasting model is established on the basis of input variable optimization. Taking the actual load of a city in Guangdong province as an example, the effectiveness of the proposed method in improving the daily load curve forecasting accuracy is verified by comparing with other methods. Results show that the model established is long-term effective, dispensing with repeated training, which is applicable for short-term load forecasting.

Key words: load characteristics, clustering, elastic net, neural network, load forecasting

靳冰洁, 林勇, 罗澍忻, 韦斌, 周姝灿. 基于负荷特性聚类及Elastic Net分析的短期负荷预测方法[J]. 中国电力, 2020, 53(9): 221-228.

JIN Bingjie, LIN Yong, LUO Shuxin, WEI Bin, ZHOU Shucan. A Short-Term Load Forecasting Method Based on Load Curve Clustering and Elastic Net Analysis[J]. Electric Power, 2020, 53(9): 221-228.

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基于负荷特性聚类及Elastic Net分析的短期负荷预测方法

A Short-Term Load Forecasting Method Based on Load Curve Clustering and Elastic Net Analysis

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基于负荷特性聚类及Elastic Net分析的短期负荷预测方法

A Short-Term Load Forecasting Method Based on Load Curve Clustering and Elastic Net Analysis

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