PV Power Forecasting Method Considering Multiple Scenarios and Differentiated Compensation Strategy

FU Hao; ZOU Hualei; XIAO Bitao; ZHOU Yan; LIU Yu

doi:10.19805/j.cnki.jcspe.2025.250253

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PV Power Forecasting Method Considering Multiple Scenarios and Differentiated Compensation Strategy

更新时间：2026-10-10

- PV Power Forecasting Method Considering Multiple Scenarios and Differentiated Compensation Strategy
- Chinese Journal of Power Engineering Vol. 45, Issue 10, Pages: 1600-1608(2025)
- 作者机构：
  
  1. 国电南京自动化股份有限公司,江苏,南京,210032
  2. 南京国电南自维美德自动化有限公司,江苏,南京,210032
  3. 南京邮电大学自动化学院,江苏,南京,210023
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2025.250253
  CLC：
- Online First：26 May 2025，
  
  Published：2025
- 稿件说明：
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付豪,邹花蕾,肖碧涛,周岩,刘宇. 考虑多场景和差异化补偿策略的光功率预测方法动力工程学报, 2025, 45(10): 1600-1608 https://doi.

org/10.19805/j.cnki.jcspe.2025.250253
付豪,邹花蕾,肖碧涛,周岩,刘宇. 考虑多场景和差异化补偿策略的光功率预测方法动力工程学报, 2025, 45(10): 1600-1608 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.250253.

org/10.19805/j.cnki.jcspe.2025.250253 DOI：

摘要

针对现有光功率预测模型在极端天气下预测精度低、天气场景特征解析方式粗糙以及动态补偿机制缺失等问题

提出了一种考虑多场景和差异化补偿策略的光功率预测方法。该方法提出混合天气类型概念

并通过构建具有优化评价功能的聚类(clustering with optimal evaluation function

COEF)算法

实现天气状态场景的自适应分类;基于极限学习机构建基础值预测模型

并阐明多场景的补偿机理

通过对不同天气场景设计针对性的误差补偿模型

实现对基础预测值的多尺度校正

提高算法的预测精度。最后

选择不同地域和气候特点的多场站实际数据进行仿真测试。仿真结果表明:与物理模型及传统机器学习算法相比

所提出的光功率预测方法在多时间尺度、多场景工况下均有更好的预测效果。

Abstract

In order to solve key problems like poor accuracy of current photovoltaic (PV) power forecasting models during extreme weather

lack of detailed analysis of weather conditions and absence of flexible adjustment methods

a photovoltaic power forecasting method that considering multiple scenarios and differentiated compensation strategy was proposed. In the method

a concept of mixed weather types was proposed

and adaptive classification of weather state scenarios was realized by constructing a clustering algorithm with an optimal evaluation function (COEF). Based on extreme learning machine

basic value prediction model was constructed

and compensation mechanism of multiple scenarios was clarified

and the multi-scale correction of the basic prediction value was realized by designing targeted error compensation models for different weather scenarios

so as to improve the prediction accuracy of the algorithm. Finally

multiple station real data from different regions with different climatic characteristics were selected for simulation testing. The simulation results show that compared with physical model and traditional machine learning algorithm

the proposed photovoltaic power forecasting method has better prediction effect under multiple time scales and multiple scenarios.

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references

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