Generalized additive short-term load forecasting model considering eigenvalue subdivision

向德军; 张维静; 冯歆尧; 杨秋勇; 蔡元纪

doi:10.3969/j.issn.1009-1831.2023.01.008

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Generalized additive short-term load forecasting model considering eigenvalue subdivision

更新时间：2026-03-05

- Generalized additive short-term load forecasting model considering eigenvalue subdivision
- Vol. 25, Issue 1, (2023)
- 作者机构：
  
  1. 广东电网有限责任公司,广州,510000
  2. 清华大学电机系,北京,100084
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2023.01.008
  CLC：
- Published：2023
- 稿件说明：
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向德军, 张维静, 冯歆尧, et al. Generalized additive short-term load forecasting model considering eigenvalue subdivision[J]. 2023, 25(1).
DOI：

向德军, 张维静, 冯歆尧, et al. Generalized additive short-term load forecasting model considering eigenvalue subdivision[J]. 2023, 25(1). DOI： 10.3969/j.issn.1009-1831.2023.01.008.

摘要

准确的负荷预测是保障电力市场有序运行的关键，针对单一因素预测环境受限、综合模型对变量的拆分及交互作用考虑欠周的问题，提出建立考虑温度、节假日、负荷等细分特征变量的广义加性短期负荷预测模型。首先，细分并测算了不同因素与负荷的相关系数，依据相关度筛选特征变量；其次，确定负荷波动基函数，针对细分特征变量分别建模，形成整体预测模型；最后，利用广州实际运行数据测算分析。实例表明，基于特征值细分的广义加性模型预测效果更佳，是短期负荷预测的一种有效方法。

Abstract

Accurate load forecasting is the key to ensure the orderly operation of the power market. In view of the limited environment of single factor forecasting and the ill- considered problems of the split and interaction of variables in the comprehensive model

a generalized additive short- term load forecasting model considering subdivided characteristic variables such as temperature

holidays and load is proposed. Firstly

the correlation coefficients of different factors and loads are subdivided and calculated

and the eigenvalues are selected according to the correlation degree. Secondly

the load fluctuation basis function is determined

and the whole forecasting model is formed according to the subdivided characteristic variables. Finally

the actual operation data of Guangzhou is used to calculate and analyze. The example shows that the generalized additive model based on eigenvalue subdivision is an effective method for short-term load forecasting.

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