考虑实时气象耦合作用的地区电网短期负荷预测建模

李滨; 陆明珍

doi:10.7500/AEPS20200216001

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考虑实时气象耦合作用的地区电网短期负荷预测建模

学术研究 | 更新时间：2025-06-16

- 考虑实时气象耦合作用的地区电网短期负荷预测建模
- Short-term Load Forecasting Modeling of Regional Power Grid Considering Real-time Meteorological Coupling Effect
- 电力系统自动化-方正平台 2020年44卷第17期页码：60-68
- 作者机构：
  
  广西电力系统最优化与节能技术重点实验室（广西大学），广西壮族自治区南宁市 530004
- 作者简介：
  
  李滨(1975—)，女，博士，副教授，硕士生导师，主要研究方向：电力系统最优化。E-mail：lizhen@gxu.edu.cn
  陆明珍(1994—)，女，通信作者，硕士研究生，主要研究方向：电网短期负荷预测。E-mail：1712302013@st.gxu.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(51767004)
- DOI：10.7500/AEPS20200216001
  中图分类号：
- 收稿日期：2020-02-16，
  
  修回日期：2020-05-20，
  
  网络出版日期：2020-06-24，
  
  纸质出版日期：2020-09-10
- 稿件说明：
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李滨,陆明珍.考虑实时气象耦合作用的地区电网短期负荷预测建模[J].电力系统自动化,2020,44(17):60-68.

LI Bin,LU Mingzhen.Short-term Load Forecasting Modeling of Regional Power Grid Considering Real-time Meteorological Coupling Effect[J].Automation of Electric Power Systems,2020,44(17):60-68.
李滨,陆明珍.考虑实时气象耦合作用的地区电网短期负荷预测建模[J].电力系统自动化,2020,44(17):60-68. DOI： 10.7500/AEPS20200216001.

LI Bin,LU Mingzhen.Short-term Load Forecasting Modeling of Regional Power Grid Considering Real-time Meteorological Coupling Effect[J].Automation of Electric Power Systems,2020,44(17):60-68. DOI： 10.7500/AEPS20200216001.

摘要

传统的短期负荷预测中并未考虑实时气象因素的耦合作用，针对此提出了考虑实时气象耦合作用的时域卷积网络短期负荷预测方法。首先，分析了各项实时综合气象指数与负荷曲线的相关性，进而构建了混合日特征量与实时气象因素的相似日选取方法。然后，引入各项实时综合气象指数作为模型输入。最后，采用能够充分考虑并包容实时气象因素与负荷“时差性”特点的时域卷积网络进行日前负荷预测建模。实验仿真以某地区电网实际负荷为例，研究表明该预测模型能够有效提升地区电网日前负荷预测精度。

Abstract

In the traditional short-term load forecasting

the coupling effect of real-time meteorological factors is not considered. To solve the above problem

a short-term load forecasting method of temporal convolutional network is proposed considering the real-time meteorological coupling effect. First

the correlation between the real-time comprehensive meteorological indices and load curves is analyzed

and then the similar day selection method of the mixed daily characteristics and the real-time meteorological factors is constructed. Moreover

the real-time comprehensive meteorological indices are introduced as the model inputs. Finally

the temporal convolutional network which can fully consider and contain the characteristics of “time difference” between the real-time meteorological factors and the loads is used to carry out the day-ahead load forecasting modeling. The experiment simulation takes the actual load of a regional power grid as an example

and the simulation results show that the forecasting model can effectively improve the accuracy of day-ahead load forecasting in regional power grid.

关键词

Keywords

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