基于深度学习的集中式光伏电站在线增量功率预测方法

杨超颖; 李慧蓬; 赵军

doi:10.11930/j.issn.1004-9649.202510014

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基于深度学习的集中式光伏电站在线增量功率预测方法

新能源与储能 | 更新时间：2026-08-04

- 基于深度学习的集中式光伏电站在线增量功率预测方法
- Online incremental power forecasting method for centralized photovoltaic power plants based on deep learning
- 中国电力 2026年59卷第5期页码：142-149
- 作者机构：
  
  国网山西省电力公司电力科学研究院,山西,太原,030001
- 作者简介：
- 基金信息：
  
  国网山西省电力公司科技项目（52053023000M）。
- DOI：10.11930/j.issn.1004-9649.202510014
  中图分类号：
- 网络首发：2026-05-15，
  
  纸质出版：2026
- 稿件说明：
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杨超颖, 李慧蓬, 赵军. 基于深度学习的集中式光伏电站在线增量功率预测方法[J]. 中国电力, 2026, 59(5): 142-149.

YANG Chaoying, LI Huipeng, ZHAO Jun. Online incremental power forecasting method for centralized photovoltaic power plants based on deep learning[J]. 2026, 59(5): 142-149.
杨超颖, 李慧蓬, 赵军. 基于深度学习的集中式光伏电站在线增量功率预测方法[J]. 中国电力, 2026, 59(5): 142-149. DOI： 10.11930/j.issn.1004-9649.202510014.

YANG Chaoying, LI Huipeng, ZHAO Jun. Online incremental power forecasting method for centralized photovoltaic power plants based on deep learning[J]. 2026, 59(5): 142-149. DOI： 10.11930/j.issn.1004-9649.202510014.

摘要

在“双碳”目标下，集中式光伏电站已成为新能源电力系统的重要支撑，但光伏发电功率受季节、天气等因素影响具有强间歇性与波动性。针对实际应用中输入数据动态演变导致模型性能衰减，且传统更新方式易引发灾难性遗忘的问题，提出一种基于深度学习的在线增量功率预测模型。该模型引入深度经验回放++（deep experience replay，DER++）增量学习机制，构建“分块特征提取-在线知识保留”双核心框架，通过补丁令牌策略捕捉多尺度周期性特征，利用自注意力机制挖掘多变量依赖关系，结合经验回放技术缓解灾难性遗忘。基于某光伏电站实测数据表明，所提模型的累计精度衰减率远低于传统模型，展现出更强的适应性与泛化能力，为集中式光伏功率在线动态预测提供了有效解决方案。

Abstract

Under the "dual carbon" goals

centralized photovoltaic (PV) power stations have become a crucial support for the new energy power syst however

PV power generation is strongly intermittent and volatile due to factors such as seasons and weather. Addressing the issue of model performance degradation caused by the dynamic evolution of input data in practical acations

and the susceptibility of traditional updating methods to catastrophic forgetting

this paper proposes a deep learning-based online incremental power prediction model. The model introduces the Deep Experience Replay (DER) incremenng mechanism to construct a dual-core framework of "block feature extraction and online knowledge retention". It captures multi-scale periodic features via a patch token strategy

utilizes self-attention mechanto mine multivariate dependencies

and combines experience replay techniques to alleviate catastrophic forgetting. Experimental results based on real-world data from a PV power station indicate that the cumulative accuracy degradation rate of the prsed model is significantly lower than that of traditional models

demonstrating stronger adaptability and generalization capabilities

and providing an effective solution for online dynamic power prediction in centralized.

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