基于结构化负荷模型的电力负荷概率区间预测

doi:10.11930/j.issn.1004-9649.202007217

中国电力 ›› 2021, Vol. 54 ›› Issue (9): 89-95.DOI: 10.11930/j.issn.1004-9649.202007217

基于结构化负荷模型的电力负荷概率区间预测

庞传军^1,2, 张波^1,2, 余建明^1,2, 刘艳^1,2

1. 南瑞集团有限公司（国网电力科学研究院有限公司），江苏南京 211106;
2. 北京科东电力控制系统有限责任公司，北京 100192

收稿日期:2020-08-07 修回日期:2021-07-28 发布日期:2021-09-14
作者简介:庞传军(1984-),男,通信作者,硕士,高级工程师,从事电力系统及自动化、人工智能技术在电力系统中的应用研究,E-mail:pangchuanjun@sgepri.sgcc.com.cn;张波(1978-),男,硕士,高级工程师,从事电力系统及自动化研究,E-mail:zhangbo7@sgepri.sgcc.com.cn;余建明(1979-),男,博士,高级工程师,从事电力系统高级应用软件研究,E-mail:yujianming@sgepri.sgcc.com.cn
基金资助:
国家重点研发计划资助项目(互联大电网高性能分析和态势感知技术，2018YFB0904501)

Probabilistic Interval Forecasting of Power Load Based on Structured Load model

PANG Chuanjun^1,2, ZHANG Bo^1,2, YU Jianming^1,2, LIU Yan^1,2

1. NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China;
2. Beijing Kedong Electric Power Control System Co., Ltd., Beijing 100192, China

Received:2020-08-07 Revised:2021-07-28 Published:2021-09-14
Supported by:
This work is supported by the National Key Research and Development Program of China (High Performance Analysis and Situation Awareness Technology of Interconnected United Power Grid, No.2018YFB0904501)

摘要/Abstract

摘要： 为了考虑电力负荷的不确定性，概率和区间预测成为电力负荷预测的重要方式之一。针对传统的负荷概率及区间预测方法没有考虑不同负荷成分的不确定性对电力负荷影响的问题，在分析电力负荷成分的基础上，基于结构化电力负荷模型提出一种电力负荷概率及区间预测方法。首先，对电力负荷的成分进行分析，针对不同负荷成分分别进行建模，构成结构化电力负荷模型；然后，基于历史负荷数据采用变分贝叶斯估计算法训练模型参数的后验概率分布；最后，基于训练完成的模型对未来负荷的概率分布进行预测，从而实现电力负荷概率区间预测。采用实际电力负荷数据进行验证，并与其他方法进行对比。实验结果表明，所提方法取得了较高的预测区间覆盖率和较窄的预测区间宽度。

关键词: 负荷预测, 负荷概率区间预测, 结构化负荷模型, 变分贝叶斯估计

Abstract: Probability interval forecasting has become one of the main methods for power load forecasting because of the uncertainties of power load. In order to solve the problem that the conventional probability interval forecasting methods cannot consider the impact of the uncertainties of different components on the power load, a structural power load probability interval forecasting method is proposed based on the time-series state space model. Firstly, based on an analysis of the components of power load, models are established respectively for different load components. And then, based on the historical load data, the variational bayesian inference is used to train the posterior probability distribution of model parameters. Finally, the probability distribution of the future power load is predicted based on the trained model, thus realizing the forecasting of the power load probability interval. The proposed method is verified using the real power load data and compared with other methods. The experimental results show that the proposed method has a higher forecasting interval coverage probability and a narrower forecasting interval average width.

Key words: power load forecasting, power load probability interval forecasting, structural power load model, variational bayesian inference

庞传军, 张波, 余建明, 刘艳. 基于结构化负荷模型的电力负荷概率区间预测[J]. 中国电力, 2021, 54(9): 89-95.

PANG Chuanjun, ZHANG Bo, YU Jianming, LIU Yan. Probabilistic Interval Forecasting of Power Load Based on Structured Load model[J]. Electric Power, 2021, 54(9): 89-95.

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基于结构化负荷模型的电力负荷概率区间预测

Probabilistic Interval Forecasting of Power Load Based on Structured Load model

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基于结构化负荷模型的电力负荷概率区间预测

Probabilistic Interval Forecasting of Power Load Based on Structured Load model

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