Weighted load forecasting method based on change trend and meteorological factors

谭风雷; 丁心志; 张军; 陈昊; 何嘉弘

doi:10.3969/j.issn.1009-1831.2022.03.011

您当前的位置：

首页 >

文章列表页 >

Weighted load forecasting method based on change trend and meteorological factors

更新时间：2026-03-05

- Weighted load forecasting method based on change trend and meteorological factors
- Vol. 24, Issue 3, (2022)
- 作者机构：
  
  1. 国网江苏省电力有限公司超高压分公司,南京,211102
  2. 云南电力试验研究院（集团）有限公司,昆明,650217
  3. 东南大学电气工程学院,南京,210096
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2022.03.011
  CLC：
- Published：2022
- 稿件说明：
移动端阅览
谭风雷, 丁心志, 张军, et al. Weighted load forecasting method based on change trend and meteorological factors[J]. 2022, 24(3).
DOI：

谭风雷, 丁心志, 张军, et al. Weighted load forecasting method based on change trend and meteorological factors[J]. 2022, 24(3). DOI： 10.3969/j.issn.1009-1831.2022.03.011.

摘要

针对采用趋势变化法预测电力负荷时，存在因未充分考虑气象因素而影响预测精度的问题，提出了一种基于变化趋势和气象因子的加权负荷预测方法。该方法综合考虑气温、湿度和天气类型3个气象因子，对基于变化趋势的电力负荷预测方法进行修正。分析了基于变化趋势的电力负荷预测方法存在的 2 个问题：未考虑外界因素对电力负荷变化趋势的影响和预测电力负荷时存在累计误差。在充分研究日内平均负荷变化率和点间平均负荷变化率的基础上，给出了算法的详细计算步骤，并将其应用于苏北某地区电力负荷的预测算例中。结果表明该方法的预测误差为 3.42%，预测精度高且数据波动小，验证了该方法的有效性和可行性。

Abstract

Arming at the problem of not considering meteorological factors when the trend change method is used to forecast power load

a weighted method of load forecasting based on change trend and meteorological factors is proposed

which is modified the power load forecasting method based on the change trend by considering the three meteorological factors of temperature

humidity and weather type. Two problems of the power load forecasting method based on the change trend including failure to consider the influence of external factors and cumulative error by trend forecasting method are analyzed. The detailed calculation steps are given based on fully studying the daily average load change rate and the average load change rate between points

which is applied to an ex-ample of power load forecasting in a certain area of northern Jiangsu. Finally

the results show that the average relative error of the method is 3.42% with high forecasting accuracy and small data fluctuating

which verifies its validity and feasibility.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Power load dispatch optimization considering dynamic response reliability

Short-term power load forecasting based on parallel sequential convolutional neural network

Short?term load forecasting method considering load behavior in different periods

Power load forecasting algorithm based on classified identification deep belief network

Short⁃term electric power load forecasting based on accumulated temperature effect and optimized support vector machine

Related Author

赵文会

钱伟强

赵罡

胡春光

张月

岳首志

许中平

林海

Related Institution

上海电力大学经济与管理学院

国网上海市电力公司浦东供电公司

国网江苏省电力有限公司镇江供电分公司

广东电网广州供电局

北京国网信通埃森哲信息技术有限公司

AI问答

⁰