基于变分模态分解和复合变量选取的短期负荷预测

周纲; 黄瑞; 刘谋海; 李文博; 胡军华; 高云鹏

周纲,黄瑞,刘谋海,李文博,胡军华,高云鹏.基于变分模态分解和复合变量选取的短期负荷预测[J].电测与仪表,2024,61(2):122-129.
ZHOU Gang,HUANG Rui,LIU Mouhai,LI Wenbo,HU Junhua,GAO Yunpeng.Short-term load forecasting based on variational mode decomposition and complex variable selection algorithm[J].Electrical Measurement & Instrumentation,2024,61(2):122-129.

基于变分模态分解和复合变量选取的短期负荷预测

Short-term load forecasting based on variational mode decomposition and complex variable selection algorithm

DOI：10.19753/j.issn1001-1390.2024.02.018

中文关键词: 短期负荷预测变分模态分解复合变量选取算法长短时记忆神经网络

英文关键词:short-term load forecasting, variational modal decomposition, complex variable selection algorithm, long and short-term memory neural network

基金项目:国家电网有限公司科技项目(5216A019000S)，国家自然科学基金资助项目（ 51777061）

作者中文名	作者英文名	单位
周纲	ZHOU Gang	国网湖南省电力有限公司
黄瑞	HUANG Rui	国网湖南省电力有限公司
刘谋海	LIU Mouhai	国网湖南省电力有限公司
李文博	LI Wenbo	湖南大学
胡军华	HU Junhua	国网湖南省电力有限公司
高云鹏	GAO Yunpeng	湖南大学

摘要点击次数: 1115

中文摘要:

精准的短期负荷预测是实现电网精益化运行和管理重要保障，但存在短期负荷波动性强、负荷预测关键影响因素选取困难等精准预测难题。利用变分模态分解将原始电力负荷数据分解为多个子序列，挖掘短期负荷波动特征的同时避免模态混叠问题，提出复合变量选取算法分析筛选影响负荷波动的关键因素，有效去除预测干扰信息并进一步简化预测模型的复杂度，通过兼顾数据短期依赖和长期依赖的长短时记忆神经网络对各子序列进行预测，并将各子序列预测结果进行叠加实现最终的短期负荷预测，据此建立基于变分模态分解和复合变量选取的短期负荷预测方法。选取2019年整年长沙市实际数据验证结果表明，提出算法在复杂外部影响因素下，能准确筛选负荷预测的关键影响因素，相比传统预测模型，提出模型结构更简单、预测精度更高。

英文摘要:

Accurate short-term load forecasting is an important guarantee for achieving lean operation and management of the power grid. However, there are difficulties in precise forecasting such as short-term load variability and selecting key factors of load forecasting. Variational mode decomposition is used to decompose the original power load data into multiple sub-sequences, to mine short-term load change characteristics while avoiding mode aliasing problems, and a complex variable selection algorithm is proposed to analyze and screen the key factors affecting load changes, effectively eliminating undesired data and further simplifying the complexity of the prediction model. Each sub-sequence is predicted through the long and short-term memory neural network that takes into account the short-term and long-term dependence of data, and merges the prediction results of each sub-sequence to achieve the final short-term load forecast, and a short-term load forecasting method is built accordingly based on variational modal decomposition and selection of complex variables. The verification results of the actual data of Changsha City selected for the entire year of 2019 show that the algorithm proposed here can accurately select the key influencing factors of load forecasting under complex external influence factors. Compared with the traditional forecasting models, the proposed model structure is simpler and the prediction accuracy is higher.

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