基于频域分解的短期负荷预测研究分析

doi:10.11930/j.issn.1004-9649.201809089

中国电力 ›› 2020, Vol. 53 ›› Issue (4): 114-121.DOI: 10.11930/j.issn.1004-9649.201809089

基于频域分解的短期负荷预测研究分析

马愿¹, 张倩¹, 李国丽¹, 马金辉², 丁津津³

1. 工业节能与电能质量控制协同创新中心（安徽大学电气工程与自动化学院），安徽合肥 230601;
2. 国网安徽省电力有限公司，安徽合肥 230073;
3. 国网安徽省电力有限公司电力科学研究院，安徽合肥 230022

收稿日期:2018-09-25 修回日期:2019-01-07 发布日期:2020-04-05
作者简介:马愿(1993-),男,通信作者,硕士研究生,从事负荷预测、光伏发电预测研究,E-mail:664328543@qq.com;张倩(1984-),女,博士,副教授,从事负荷预测、智能优化算法研究,E-mail:qianzh@ahu.edu.cn
基金资助:
国家重点研发计划资助项目（2016YFB0900400）；国家自然科学基金资助项目（51507001）；安徽大学2015博士科研启动项目（J01001929）

Research and Analysis of Short-term Load Forecasting Based on Frequency Domain Decomposition

MA Yuan¹, ZHANG Qian¹, LI Guoli¹, MA Jinhui², DING Jinjin³

1. Collaborative Innovation Center of Industrial Energy-saving and Power Quality Control (School of Electrical Engineering and Automation, Anhui University), Hefei 230601, China;
2. State Grid Anhui Electric Power Co., Ltd., Hefei 230073, China;
3. State Grid Anhui Electric Power Co., Ltd. Electric Power Research Institute, Hefei 230022, China

Received:2018-09-25 Revised:2019-01-07 Published:2020-04-05
Supported by:
This work is supported by the National Key Research and Development Project of China (No.2016YFB0900400), National Natural Science Foundation of China (No.51507001), Doctoral Research Foundation of Anhui University under Grant (No.J01001929)

摘要/Abstract

摘要： 为研究频域分量预测法对短期负荷预测精度的影响，利用频域分解算法分解原始负荷数据，将数据分解为4个部分：日周期、周周期、低频和高频分量。其中，日周期、周周期分量用Elman神经网络预测；低频分量采用随机森林预测；高频分量则使用Mallat算法二次分解，分别得到低频部分和高频部分，选取低频部分做训练样本与Elman神经网络结合预测高频分量；将各个频域分量结果重组，实现电力负荷的高精度预测。以某地市实际负荷数据为例进行仿真，将该方法与Elman神经网络法、随机森林法及频域分量预测法的预测结果对比，验证所提方法可以有效提高精度，减少预测值和真实值的离散程度。

关键词: 负荷预测, 频域分解, Elman神经网络, 随机森林, Mallat算法

Abstract: To study the effect of frequency domain component prediction on the accuracy of short-term load forecasting. This paper proposes to decompose the original load data based on the frequency domain decomposition algorithm,and decompose the data into four parts: daily cycle, weekly cycle, low frequency and high frequency components. Among them, the daily and weekly cycle components are forecasted by Elman neural network; the low-frequency components are forecasted by random forest; the high-frequency components are secondarily decomposed using the Mallat algorithm to obtain the low-frequency part and the high-frequency part respectively. The low-frequency part is selected as the training sample and is combined with the Elman neural network to predict the high-frequency components. The results of each frequency domain component are recombined to achieve high-precision prediction of power load. The actual load data of a certain city in Anhui Province is taken as an example. Compared with the forecasting results of Elman neural network, random forest method and frequency domain component forecasting method, the proposed method can effectively improve the accuracy and reduce the degree of dispersion of predicted and true values.

Key words: load forecasting, frequency domain decomposition, Elman neural network, random forest, Mallat algorithm

马愿, 张倩, 李国丽, 马金辉, 丁津津. 基于频域分解的短期负荷预测研究分析[J]. 中国电力, 2020, 53(4): 114-121.

MA Yuan, ZHANG Qian, LI Guoli, MA Jinhui, DING Jinjin. Research and Analysis of Short-term Load Forecasting Based on Frequency Domain Decomposition[J]. Electric Power, 2020, 53(4): 114-121.

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https://www.electricpower.com.cn/CN/Y2020/V53/I4/114

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基于频域分解的短期负荷预测研究分析

Research and Analysis of Short-term Load Forecasting Based on Frequency Domain Decomposition

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基于频域分解的短期负荷预测研究分析

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