综合预测模型及其单一预测方法的联合参数自适应优化

LI Ye; KANG Chongqing; CHEN Xinyu

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综合预测模型及其单一预测方法的联合参数自适应优化

更新时间：2025-08-13

- 综合预测模型及其单一预测方法的联合参数自适应优化
- Automation of Electric Power Systems Vol. 34, Issue 22, Pages: 36-40(2010)
- 作者机构：
  
  电力系统国家重点实验室，清华大学电机系,北京市,100084
- 作者简介：
- 基金信息：
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- Published：2010
- 稿件说明：
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LI Ye, KANG Chongqing, CHEN Xinyu. [J]. Automation of electric power systems, 2010, 34(22): 36-40.
DOI：

LI Ye, KANG Chongqing, CHEN Xinyu. [J]. Automation of electric power systems, 2010, 34(22): 36-40. DOI：

摘要

参数自适应是提高预测精度的一种有效手段，单一预测方法的参数自适应方法和综合模型的权重优化方法已有较多研究，但是一般都是对两者独立研究的。将综合模型中的单一预测方法的参数自适应和综合模型权重的自适应结合起来，提出联合参数自适应优化的概念，并且以短期系统负荷预测为基础，对这2步参数自适应过程进行了建模、分析和总结，给出了2种有效的联合参数自适应策略，通过算例进行了论证。所述方法将为涉及综合预测模型的参数自适应问题提供更加完善的理论依据和选择策略。

Abstract

Adaptive training is an effective method to improve the precision of the load forecasting. Now there are many researches on the adaptive training in a single forecasting method or in an integrated model. However， the two parts are separated. Combining the adaptive training in single methods and that of the integrated model， this paper releases the concept of joint adaptive training（JAT） based on short-term load forecasting. It also builds the model of the problem and gives out analysis， summery and strategies. Besides， through a real sample， this JAT method is proved effective. In this way， this paper will provide a relative well-reasoned theory and concrete strategies to those applications of adaptive training of both the integrated model and single methods at the same time.

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